Maximising (Re)Usability of Library Metadata Using Linked Data
Gómez-Pérez, Asunción
Linked Data (LD) and related technologies are providing the means to connect high volumes of disconnected data at Web-scale and producing a huge global knowledge graph. The key benefits of applying LD principles to datasets are i. better modelling of datasets as directed labelled graphs, ii. structural interoperability of heterogeneous resources, iii. federation of resources from different sources and at different layers inlcuding language annotation, iv. a strong ecosystem of tools based on RDF and SPARQL, v. improved conceptual interoperability due to strong semantic models such as OWL and shared semantics due to linking and vi. dynamic evolution of resources on the web. In this talk, I will explore challenges related with the (Re)Usability of library linked metadata in the field of cultural heritage and for other purposes. I will argue that for maximizing (re)use of library linked metadata it is crucial to represent core aspects related with Linguistic, Provenance, License, and Dataset metadata. A proper representation of these features using W3C standards and the use of W3C best practices and guidelinesfor multilingual Linked Open Data: i. produce better library linked metadata that could be used later on for diagnosing and repairing other external resources; ii. facilitate rights management, and consequently the access and reuse of metadata and data delivered under different license schema; iii. enable navigation across datasets in different languages thanks to the exploitation of links across multilingual data; iv. help data providers and data consumers to go a step further when cataloguing, searching and building cross-lingual applications that use open library linked metadata; v. increase exploitation when library linked metadata will be used with licensed (open or closed) linked data in other domains. I will also present approaches that use datos.bne.es library linked metadata with geographical information to produce new insights and innovation. SWIB15 Conference, 23 – 25 November 2015, Hamburg, Germany. #swib15
 Okay, yes, as Jojen already said, this is a work that has been done in collaboration with the Spanish National Library and in particular with a great development realized by Daniel Villasuedo who is now in Madrid, continuing with the developments. So the motivation of, so I will start this talk with motivation and making clear how to deal with other kinds of metadata that are related with language, provenance and license which are extremely useful when trying to use a library link and metadata in final application. I will also review the link data process followed with examples from the Spanish National Library and at the end I will highlight some uses of the library link metadata in other fields and how library metadata can be integrated, can be reused with link data coming from the geographical and education domain. So we live at this moment in a world of digital data, so we have many providers, city halls, governments, devices, also people generating metadata and a lot of data being generated in social media. And this metadata is available and this data in different domains, governmental energy, library, geography and also it appears in different formats. It is not only the March 21 records that you are used to work with but also we have data in formats, databases, geographical data, in a special format given by sensors and also in link data. And the most important part is that the data is language oriented in the sense that we are generating thousands of link data, datasets in the cloud for other purposes and in many of the languages and not all the data that we have is open. At some time we need to work with closed data. So we live in an ecosystem of resources already in different silos that belongs to different complementary domains that are in different formats, in different languages and store in repositories with different metadata. And of course the life of this program is really hard when trying to use many API and services for querying the metadata available. So the data sources that we are using are complementary but at some point they are not connected. So the good things of link data is that it allows the uniform access to the data that has been transformed into RDF. When working application with based on link data we need to agree on vocabularies for describing the metadata and also the domain data. We have a language and a standardized language proposed by W3C RDFS for writing down the models and also RDF for the data. We have Sparkle as a standard query language and we have many standardized non-properitary APIs for using the data that has been made available. But the power of link data you already know is the links that we have with other resources and these links could be to data in the same language or data that appear in different languages. But for the end user that is trying to use the link data format, quality is something very important and before starting using data sets that are available there are many questions. Related with who generated the data set, how the data set was transformed, when the data set was created in order to see if it is providing updated data. Is that the last version of the data set? Which one is the license of the data set? Is the license clearly stated in the data set? In some graphs? In some triples? In which formats the data are delivered and if data are monolingual or multilingual? So at the end in order to discover the quality of a particular data set, the metadata really matters. I mean metadata matters because metadata about data could express provenance information, could express license information, could express language information, privacy if we are dealing with data related to people. And of course there are other types of metadata related with geolocation, spatial time that needs to be represented. So mainly in this talk what I am going to present in the first part is how to represent metadata related with language, provenance and licenses. And how this metadata could be used later on for developing some kind of applications or in some new cases. So as Joao mentioned before, I mean I am coordinating a project named Leader which is about linguistic link data. And we already know that link data is multilingual and we know that the library is stored multilingual data. So let's say if you go to VIA you can see here Miguel de Cervantes Saavedra, one of the famous writers from Spanish literature in many different languages. If we go to the Spanish National Library we also have some variants for representing Miguel de Cervantes in different languages. In that case you can see that we are not saying in which language is each variant, in the case of VIA it appears because of the flags. One in the French National Library we can take Don Quixote which is one of the famous books written by Cervantes and there we can see the translation of Don Quixote into different languages for French and Spanish. It is the language but for the other titles we need to discover which one is the language. And of course we have also the resources like the Vipidia which are providing links from other data sets where we can find information about Miguel de Cervantes in different languages. So when we are talking about linguistic link data, so these are examples that show that we really need to deal with language. So several years ago, two years ago, three years ago we started to think about how to represent language information in link data and how linguistic information could help in order to deal with language. So we came to this idea of the linguistic link data cloud. Three years ago it was a dream, now it is a reality because we have more than 150 resources on the linguistic link data cloud and the linguistic link data cloud is a subset of the link open data cloud. Just focus on the linguistic domain with many types of resources because there we can find corpora, terminological databases, linguistic data categories, typological databases, lezikons and also bilingual dictionaries or monolingual dictionaries. So in this linguistic link data cloud, language resources are connected and are connected with other language resources. And this is very important because linguistic link data will allow or is allowing at this moment the interconnection of words and linguistic information that appear in different languages and also it enables the leziklicization of data on the web. And that could be in the link data format or that could be in other formats. And it can also help with the multilingual search. And so let's say, so the linguistic link data could help to translate the same term, a book title, an author name, a place in different languages and could also help to deal with acronyms. And we'll see later on how we have used these linguistic link data for building the Spanish National Library portal. So this is the core of the linguistic link open data cloud. There are resources like the Wikipedia. We also have resources like the Pabellet. We also have resources like Lezinfo. So this part of the cloud is full of the aperitif dictionaries just translating or representing the RDF dictionaries that are between Spanish and French, Spanish and English, Catalan, and Italian and so on and so forth. So what is Babelnet? Babelnet was built by Roberto Naviglia, the Sapienza University, and it is a multilingual lezikon that has been built by merging resources of different kinds. So Babelnet has been used using Wordnet, Wikipedia, Wikidata, Omega Wiki and so on and so forth. So the same content and the same entity in Babelnet is expressed in tens of languages. So right now version 3.5 of Babelnet already has covered 272 languages and more than 14 million entries. So 6 million contents and 7.7 name entities and you can see there also 1 million images, test all definitions and so on and so forth. So the first thing, the best thing to see how Babelnet works is just going into Babelnet. So if we go to Babelnet we can see that from Miguel de Cervantes for instance I could get a translation from Miguel de Cervantes Avedra in all these languages. So these resources already exist and we can find in this resource information about Miguel de Cervantes which is Miguel is an author, a Spanish writer, he was a Spanish and the main works were Don Quixote de la Mancha for instance. We can also see some images about Miguel de Cervantes and the most important part now is that we can get information about Miguel de Cervantes and you can see how many languages. So how many languages and also knowing in which language is each translation. So going back to the presentation, so in the linguistic linked license data, so we are talking about lezikons like Babelnet, okay, corpora, monolingual, bilingual dictionaries. We are representing this information in RDF using a model named lemon that has been proposed by the W3C consortium and lemon is for representing lezikons and also we are proposing using the NLP interchange format for representing corpora. A license is important because some resources could be opened and some resources could be closed and we have also the open digital right language also proposed or being proposed by the W3C for representative licensing information in a machine readable way. So how do we represent this linked data which is behind Babelnet and other resources. So the first thing or the easiest thing is that to represent in the traditional way using the annotation properties in RDF. So in that particular case if I take the identifier of Miguel de Cervantes at the Spanish National Library the labels in different languages could be represented in this way. But if I want to use richer models to represent linguistic information for more demanding application like let's say for instance natural language generation I need to use lemon. Because Miguel de Cervantes is an author and is the author of Don Quixote in that case what we are representing in this RDF is the property author of and you can see that the property author of has two lecicalization to representation in Spanish. One is author there in the case of the masculine or in the case of feminine we could say is author. So let's say although the relation between the author and the work is author of I mean if I'm trying to use this property for other issues in the Spanish I need to do I need to deal with the gender. So in that case the linguistic data and the knowledge represented in lemon could help us to represent this information. But also lemon can help us to represent translations in the sense that if I have for instance a lecicon in Russian and the form of Miguel de Cervantes in Russian is in this way and I have also the lecicon in Spanish and the written representation for Miguel de Cervantes in this way I could link let's say these translations in lemon using some basic extensions that has been done and built in Lithuania and also consensuated at W3C. So let's say we can go beyond the annotation properties and labels and we can use more complex models if we want to prepare our link data application for other type of purposes and to make them at some point multilingual. The other important part is that we also need to add language information to the dataset description. I mean if we don't say either in void or in the cat in which language is the dataset a program need to discover the language and this is not difficult and it is a matter of introducing this RDF in these links into the dataset description. So at the end what we have is that we have the cloud the normal cloud the open data cloud and we also have the linguistic cloud and as you can see in this slide we are dealing again with vocabulary domain vocabulary for the link open data cloud, lemon, ontolex, nif for the linguistic link data cloud but the crucial thing is that and the important thing is that we have the same language because everything is represented in RDF and RDFS we have also the same query language Sparkle and we have also standardized non proprietary APIs for dealing with the domain data and also with the linguistic data and of course linguistic data link data is linked with other resources. So let's go now to the Spanish National Library portal and let's work with Miguel de Cervantes and what the portal is provided everything at this moment is in Spanish so you can feel the multilinguality issues that many non English speakers we deal with sometimes. Okay so we are collecting information from Wikipedia we are presenting the books the works from Miguel de Cervantes according to their importance so the first one is Don Quixote de la Mancha and the second one is Novellas and Hemplares so an image means that we click there we can retrieve the PDF for of this book and we also have the works about Miguel de Cervantes okay but let's go into this part so this in this information we can get the information of Miguel de Cervantes in different languages so now I could search in Arabic and now I would retrieve information about the Miguel de Cervantes so at this moment the search is prepared in order to allow the I mean different visitors to deal with querying their own language so now of course I mean I don't in order to get the books so we need this needs more more improvement we are working on that but let's say now if we go into Miguel de Cervantes Savedram and we will see and I will make a query so I will make a query about Don Quixote de la Mancha in order to show you how we have done I mean how to use it how to use it so I don't know if this is a matter of the library of there are many people now trying to get connected to the Spanish National Library through internet but please I would appreciate if you don't use internet because otherwise we I will take some time in the presentation so now I can retrieve the Spanish one of the Spanish book or I could retrieve the books in German it is quicker you can believe me and now I can retrieve you can see all the books that the Spanish National Library has written in German about Don Quixote de la Mancha I will click in this one that provides the digitalization I need to go to the to the digital library and now in two or three clicks you can see immediately that you can start reading Don Quixote de la Mancha in in in German okay and this is thanks to this kind of language features that Daniel Villa has implemented in a very efficient way okay so so now we we can expand the information if we have language information on the metadata we can expand the information based on all language so let's see if we go to our internet you can see several translations for for for La Cité Antique and we can see the Cité Antique is the ancient city okay so we can get several translations that are not available in the Spanish National Library or in the Spanish National Library and with this information from BubbleNet we can get the same information about the the author of this book in in so we can go to the DVD and to search about the the author if I go to the Spanish National Library just looking for you that is and T was which is there we can see here that we retrieve a book La Cité Antique which is in in French okay and then we can go to the French National Library and from there we can retrieve the digital the digitalized resource and then we could start reading in French because this book is not digitalized in the Spanish National Library okay so by by using BubbleNet I can I can also retrieve information in in other in other languages so so at the end we are moving forward towards a multilingual content aggregation of scenario where data a metadata coming from from different sites can be integrated so at this moment we have this multilingual information which is complementary but but we don't we are not providing the services so just by by including the the language information in the in the in different data sets I mean we are closer to this kind of application and aggregation so the linguistics data also helps with alternative names and and titles so for instance if I go to the if we go to the Spanish National Library and we looked for United United Nations you can see that the first book that we retrieve is the Naciones Unidas which is so so so the entity the first entity is Naciones Unidas because this is the way that we name it in Spanish and also because the Spanish National Library has many books about Naciones Unidas and there we can see more information and you can see in the fifth position we get or in the fourth and fifth position we get the United Nations book and books if we if we make the query based on an acronym which is ONU in Spanish ONU is the acronym for Organización de Naciones Unidas which is the the name in the in the Spanish then we can retrieve entity we can retrieve information about Naciones Unidas about the entity or we could get books related with the United Nations and why is that because we have again behind some kind of linguistic data so there we are expressing in RDF and in lemon again the acronym UN is an acronym it has a canonical form and but the labels are UN ONU in Spanish and this canonical form it has a written representation which are these two in the same way I could say that they have United Nations which is a multi-word expression has a canonical form has an abbreviation which is UN and also has a label which is United Nations in English and in this canonical form I represented the United Nations in English and Spanish and I could add many other names so so so you can see that I'm using the in that case lemon for representing this this information so thanks to this code we can retrieve the and we can query with alternative names also and so the let's go now to move into provenance so provenance is really important in order to provide metadata information about when the resource was created how the resource was created and so on so forth so we have some some standards proposed by W3C which is problem in order to represent the process-centric provenance so let's say in that case if if I have a particular file and I could go that code could go through some process then I can generate a new file let's say version one of these of this file and we also have the resource provenance in order to to to include the description the creator the creation date the rights of a particular file and we have their the Provo, Premise, European Analytics Model, Dublin Core and so on so forth and we also have metadata provenance which is provenance about the provenance already introduced by by W3C so so let's see with this with an example so in if in that particular case I'm representing the March 21 dataset of the Spanish National Library that belongs to the Spanish National Library which is has that has been defined as an agent in the Provo ontology I also have the creation date and the creation date and time and also we have the license I also have information about the conversion process when it started when ended and which tools or software were using the transformation process so in that case just because we are transforming March 21 records into our DFS using using Marinebag which is a tool developed by by Daniel Villasueva so in that particular case so I am writing that the the the conversion process was done using Marinebag which is an agent which is again an agent in the Provo in the Provo ontology and and also I have the the license for for for this dataset and of course we have at the end the metadata provenance saying that all this process is attributed to the Spanish National Library okay so now if I represent in our DFS I can also query in a sparkle the provenance information so another important part is that the the license information I mean let's say the the in in data I mean not all the data is open we can also deal with closed data and even though we can combine open data and closed data and if we want to combine open data and closed data we need to to have the license information also written in in in our DFS okay if we make our set the query how open is the link open data cloud we will be surprised because in the open link data cloud all these nodes in white means that that the license is not specified okay so I forgot to write about the license and and also in the link open data cloud we can see some bubbles in in in orange and red which means that the data set has some assets restrictions or that the data set is is closed so so as you can see so the majority of the link open data cloud doesn't specify the license or some data sets appear with a closed license which is surprising so how can we represent the license information so it will represent the license information inside the web page this can be understood and read by by humans but if we want machines to to to use and to process the license information we need to represent this license in ODRL in that case the open digital rights language and there we can express who can cannot must at what which resource and how okay so this is a model this is an ontology for representing again the license information so we can provide and describe licenses for vocabularies for ontologies and we can also provide licenses for for data sets so so we have licenses or we can represent in RDF CC0 we can also represent in RDF that closed data which is published but has not open license we can also represent linked private data this is linked data that we have but we don't advertise and make publish to the community and we can find many many many data sets with without the explicit license and this is a real nightmare because we can see that we cannot we cannot use so just because of that in the framework of one Spanish project and this work is being developed by by Victor Victor Rodrigues don't tell which is a postdoc in in our group we we are working with RDF licensing support so we have the data model based on our ODRL we are we have a data set called RDF license where we have the description of licenses in RDF and we have services like Lithentius for dealing or analyzing compatibility between different type of license and also we have tools that allows conditional access to tooling data so when we are representing the the the license information in ODRL we we can't say or we can state for instance that the license is defined for the whole data set for a particular graph for an ontology for a class for a triple or even though for for a mapping and there are some actions that people can do or cannot do they can derive translate distribute print and so on so forth there are some parties are people that could that could allow to do the action like for instance we could say one individual or that the the license is for a particular organization and we can also explain some constraints saying that you can use if you attribute or you can use if you live in a particular city or you can use in a particular time frame so in this RDF license data set we have already represented 100 almost 150 licenses they have permanent URIs so I really recommend to you to point to this data set and and in order to to express in RDF the the the license and if you want to read more I mean there is a paper over there that you can read and of course we will be glad to help with this with this topic so let's see so if we want to represent represent a creative common a license in that case a license that has no party and no constraint this would be the code the the RDF code for for that so this is the permanent URI for for for the license and there you can see the the permissions of distribution derivative works and reproduction on and it is constrained to the duty of making clear the the the attribution for instance so how do I use this license in my data set it's very simple the only thing you have to write is my data set license and in that case to include the URI of the the license okay so I can be and I can make more complex things in the sense that the reproduction of a data set is limited until the end of the year after paying 15 euros so there I have my data set I give permission to reproduce only if you pay I pay 15 euros and there at the end you can see the constraint until the end of the the year so I can also express licenses for classes and and context and this can open new business models for for for the use of library link data so I could say okay all book are offered for free new books will be sold for five euros so so in that in that case you can see that for for the the the class or book every I can do any kind of action and reproduce and also but however for the new books I can do the action only if I pay the five the five euros and also I can express more complex a question license is like you can't pay the whole 50 euros for the whole data set or I can't pay 0.01 euro for instance if I want to get that trip okay so the granularity the level of granularity and the tail without the URL is really impressive if we are using this this ontology so this model that we have already built so at the end what we have again is that we have the cloud we have the linguistic cloud we have the license represented in in in our gf so everything is in our df everything is in queries could be in a sparkle open api's and non-propellery api's and also linking this information with with other parts so okay so now I will forget now about I will go back now to the second part of the talk which is the process being followed for for for generating library link data and I will take a sample from the from that was from the Spanish National Library so there is a journal paper which is already there that you can read if you want to see more more the text the the first thing to say is that the ontology engineering group and the Spanish National Library is a team a team working composed by by cataloging services people working cataloging services in the digital library and researchers working in the ontology engineering group so so thanks to to the excellent collaboration between these two institutions we have this Spanish National Library portal the the methodology that we have followed is the classical one in building linked data applications specification modeling rdf generation data linking publication and exploitation and data curation is mainly mainly done at the beginning during the specification modeling and an rdf generation this is an experience from other domains where the process of linking data helps to improve the quality of the data in the in the in the original in the original source so when when just some some data about the the Spanish National Library data sets so the records were in March 21 format we had 4.5 million bibliographical records 4.5 million authority records and this was the version the last version we took in in September 2015 so as I said before the we wanted to have the the the team from the Spanish National Library in board the catalog is from the very beginning Ricardo Ana Mar and and also we wanted to make the we wanted to include the multilingual aspects from the very beginning and we started in the first round the collaboration with with Ibla so so so there in this slide you can see the that everything has been transformed into into rdf using marimba and marimba also links with other library data sources and also with dvp so for for modeling the the ontology in the Spanish National Library we we have done it in two phases in the first phase we reuse the urize from the standard vocabularies mainly Ibla fever frat and now in the second version of the ontology portal we integrated we built an ontology which is the b&e ontology and that reuse and links the terminologies from the from the from other resources why why did we build this ontology because we wanted to provide stability in the use of the the ontology and the the data that be a b&e domain the second is that we can document the the ontology in a central document we can enrich and provide new properties and and relate and relationships for instance those that are related with the Spanish legal deposit we can we can have the control over the description and and labels in particular for for visualization we could deal with all these multilingual aspects because the the ontology is described in English and Spanish and also to allow content negotiation for to be used by different type of API so if you want to go to the and to to see the ontology it is available under this domain so this was the first version of the the ontology where we reuse many classes from from fever so now we have the the ontology which is you can see it is the same but just persisted in the in the in the Spanish National Library at least in this high-level view so in order to promote and to explode alternatives for for names and and touch on titles so what we have done is in to deal with investing alternatives mainly related with acronyms and and translation so for instance for person we have the authority name and we also have the variant names for for work we have the the authority title and the variant titles and for organization we have the authority name and the variant names so this is dealt with as I some of these things are built as I mentioned before for the G so you can imagine so the power of the the the power of the ontology is later on transferring to the the user interface because the user interface is using many of the relationships that we have in the in the in the ontology so the third step is the generation of of the the mappings or sorry the generation in RDF so from these mark 21 records we need to to establish how this mark 21 records will be transformed into the the ontologies that that a particular author is a is a person and for that our friends from the Spanish National Library built the the mapping and then Marine Bap processes the mapping and generated the the RDF code instantiated the the the the ontology for the links we identify where were the which one where the data the data to be linked with and possibly you have already seen this is light many many times so we created many same as links with the other libraries it should be good if at some point all the libraries start linking together and reversing the the same as links and for publication we have used this is something new we have combined the cat and and void in order to include the licensing formation provenance information with provok language information using lesbo and also allowing three access mechanics in particular a sparkling point and data dumps so the data that the any that is a is a catalog and it is composed of seven data sets with different distributions and and you can't see also the the profile in these in this way so so they are here you can see the information the decad graph with information about the seven data sets the catalog distribution and so on so let's go so in this part you can see that this is the way that we have been using for representing the licensing formation so there we will have a we have a link to the RDF we already have a link to the RDF license data set we also have the description in one almost 200 language using lesbo we define the BNE as a publisher in the catalog as an agent so if we click in the BNE we go into the web page of the that is when a with that described the biblioteca nacional de spain with different books and works and so on so forth and and also you can see here that the the catalog contains seven data sets and this allows us also to to help a lot in the in the when building the new interface because now we can't provide better access if people are looking for people entity works and and things okay so and this is the way of the distribution in and also the to describe the different licenses also at the level of the the data set okay so and at the end the exploitation through the portal website and services so the for exploitation we we we have created a waiting function that uses the the ontology and also the incoming and oncoming outgoing links from the from the notes in the ontology that and we use also from the data that we have in the sparkling point also a string similarity in order to rank so if you start writing the name of a of an author so immediately the first one will be the the most famous one or with more books and works in in the Spanish National Library so for instance if I start with Lopez so in that case there is a big difference between the first one which is Lopez de Vega which is a famous writer and the second one is also Lopez de Rueda which is another one okay so so now the results of the of the social you can see here the the volumetry so in total we have four as I mentioned before for 4.5 million records of authority by biographics we have this number of triples more than a one million links with the Biaf Sudok the German National Library Libris the Wikipedia the French National Library and also with Geolink data Geolink data is a link data application based on the geographical domain okay how can we use the library link metadata of the library link linked data sorry I forgot to translate so the the the first one is to validate and and and and reach registry that are in March 21 records in other Spanish national in other Spanish libraries that are part of the long tail of of libraries okay so this is this is one of the uses because we have metadata curated I mean all this metadata which is created in Spanish National Library can be helped can be used for for for enriching other 20 mark 21 records in other library so so this is an example of the long tail which is a unespecialized forum for rare editions so you can see one famous Spanish singer from the south singing flamenco and this kind of things and and there you can see that that the person wrote okay just looking at the Spanish National Library I found works from this person that I didn't I didn't know we also found newspapers that are also pointing to to some information from the Spanish National Library metadata another use is something related with citizen science in that particular case we we built a prototype of an application named a street detective and the goal was to improve data quality and access to cultural data in in in a particular city so what we did was to build an application that have the Zaragoza map on the back and and also we have the Spanish National Library portal and Oscar Kortso one of the the vice director of the group I mean he made a challenge with kids and children and and then by using games I mean the children between 10 and 15 years old they were just linking are based on some questions they were linking streets with the authors of the Spanish National Library okay so so in order to have information on the map linked with the with Spanish National Library and an Oscar Kortso got a Fujitsu price this year just because of this application so imagine now just based on this application we can build a more complex case where we can integrate cultural data and geographical data and this is the the a neighborhood in Madrid where many of the streets have and names of famous Spanish writer writers so so I could get information about this neighborhood just going into Wikipedia I could get information about this museum just going into Wikipedia but if I search in the Spanish National Library about Museo Lope de Vega immediately I could retrieve some books about that or the same for Ateneo de Madrid with pictures images and so on so forth or for the author of Lope de Vega or the same for the street of Tervantes so in that case I would navigate instead of using wiki web pages using the web of data the same if I want El Quixote road this is the road in in in Google Maps but if I search by Ruta Don Quixote which is the Spanish translation I would get several books about that tell me about about the route and and if I want to search locations related with El Quixote in that case I have El Tavoso over over here and I have Alcázar de San Juan so I could get information about that or I made a mistake with the animation or if I could search by Tavoso Don Quixote and Tavoso Dulpina and then I would retrieve information about books writing about El Tavoso but under a particular context which is Don Quixote or Dulpina I can also based on the metadata just from the metadata in images to retrieve some full test in the from the National Library because the some PDFs are a viral okay so which one is the message to take home that a library in data can be easily integrated in in other with all the data in different type of applications so two reams geography education are the first one to explore data provides providers should include language provenance and license metadata in their data sets not only in the March 21 records that of course you do but also when you generate into the the the RDF and finally it's important to say that the benefits of adding language provenance and license metadata in the data set reduce the time and cost of identify the language in resources and terminology and allows you to point to two other terminologies it also it also fostered the aggregation and enrichment of data across complementary domains you have already seen some examples it also has the data curation because you can assess your data against other data to see if you are providing the same information and also improve the precision and recall in information retrieval and search so thank you very much and I'm available for any questions that you already have
 Okay, yes, as Jojen already said, this is a work that has been done in collaboration  with the Spanish National Library and in particular with a great development realized by Daniel  Villasuedo who is now in Madrid, continuing with the developments.  So the motivation of, so I will start this talk with motivation and making clear how  to deal with other kinds of metadata that are related with language, provenance and  license which are extremely useful when trying to use a library link and metadata in final  application.  I will also review the link data process followed with examples from the Spanish National Library  and at the end I will highlight some uses of the library link metadata in other fields  and how library metadata can be integrated, can be reused with link data coming from the  geographical and education domain.  So we live at this moment in a world of digital data, so we have many providers, city halls,  governments, devices, also people generating metadata and a lot of data being generated  in social media.  And this metadata is available and this data in different domains, governmental energy,  library, geography and also it appears in different formats.  It is not only the March 21 records that you are used to work with but also we have data  in formats, databases, geographical data, in a special format given by sensors and also  in link data.  And the most important part is that the data is language oriented in the sense that we  are generating thousands of link data, datasets in the cloud for other purposes and in many  of the languages and not all the data that we have is open.  At some time we need to work with closed data.  So we live in an ecosystem of resources already in different silos that belongs to different  complementary domains that are in different formats, in different languages and store  in repositories with different metadata.  And of course the life of this program is really hard when trying to use many API and  services for querying the metadata available.  So the data sources that we are using are complementary but at some point they are not  connected.  So the good things of link data is that it allows the uniform access to the data that  has been transformed into RDF.  When working application with based on link data we need to agree on vocabularies for describing  the metadata and also the domain data.  We have a language and a standardized language proposed by W3C RDFS for writing down the  models and also RDF for the data.  We have Sparkle as a standard query language and we have many standardized non-properitary  APIs for using the data that has been made available.  But the power of link data you already know is the links that we have with other resources  and these links could be to data in the same language or data that appear in different  languages.  But for the end user that is trying to use the link data format, quality is something  very important and before starting using data sets that are available there are many questions.  Related with who generated the data set, how the data set was transformed, when the data  set was created in order to see if it is providing updated data.  Is that the last version of the data set?  Which one is the license of the data set?  Is the license clearly stated in the data set?  In some graphs?  In some triples?  In which formats the data are delivered and if data are monolingual or multilingual?  So at the end in order to discover the quality of a particular data set, the metadata really  matters.  I mean metadata matters because metadata about data could express provenance information,  could express license information, could express language information, privacy if we are dealing  with data related to people.  And of course there are other types of metadata related with geolocation, spatial time that  needs to be represented.  So mainly in this talk what I am going to present in the first part is how to represent  metadata related with language, provenance and licenses.  And how this metadata could be used later on for developing some kind of applications  or in some new cases.  So as Joao mentioned before, I mean I am coordinating a project named Leader which is about linguistic  link data.  And we already know that link data is multilingual and we know that the library is stored multilingual  data.  So let's say if you go to VIA you can see here Miguel de Cervantes Saavedra, one of the  famous writers from Spanish literature in many different languages.  If we go to the Spanish National Library we also have some variants for representing Miguel  de Cervantes in different languages.  In that case you can see that we are not saying in which language is each variant, in the  case of VIA it appears because of the flags.  One in the French National Library we can take Don Quixote which is one of the famous  books written by Cervantes and there we can see the translation of Don Quixote into different  languages for French and Spanish.  It is the language but for the other titles we need to discover which one is the language.  And of course we have also the resources like the Vipidia which are providing links from  other data sets where we can find information about Miguel de Cervantes in different languages.  So when we are talking about linguistic link data, so these are examples that show that  we really need to deal with language.  So several years ago, two years ago, three years ago we started to think about how to  represent language information in link data and how linguistic information could help  in order to deal with language.  So we came to this idea of the linguistic link data cloud.  Three years ago it was a dream, now it is a reality because we have more than 150 resources  on the linguistic link data cloud and the linguistic link data cloud is a subset of the link open  data cloud.  Just focus on the linguistic domain with many types of resources because there we can find  corpora, terminological databases, linguistic data categories, typological databases, lezikons  and also bilingual dictionaries or monolingual dictionaries.  So in this linguistic link data cloud, language resources are connected and are connected  with other language resources.  And this is very important because linguistic link data will allow or is allowing at this  moment the interconnection of words and linguistic information that appear in different languages  and also it enables the leziklicization of data on the web.  And that could be in the link data format or that could be in other formats.  And it can also help with the multilingual search.  And so let's say, so the linguistic link data could help to translate the same term,  a book title, an author name, a place in different languages and could also help to deal with  acronyms.  And we'll see later on how we have used these linguistic link data for building the Spanish  National Library portal.  So this is the core of the linguistic link open data cloud.  There are resources like the Wikipedia.  We also have resources like the Pabellet.  We also have resources like Lezinfo.  So this part of the cloud is full of the aperitif dictionaries just translating or representing  the RDF dictionaries that are between Spanish and French, Spanish and English, Catalan,  and Italian and so on and so forth.  So what is Babelnet?  Babelnet was built by Roberto Naviglia, the Sapienza University, and it is a multilingual  lezikon that has been built by merging resources of different kinds.  So Babelnet has been used using Wordnet, Wikipedia, Wikidata, Omega Wiki and so on and so forth.  So the same content and the same entity in Babelnet is expressed in tens of languages.  So right now version 3.5 of Babelnet already has covered 272 languages and more than 14  million entries.  So 6 million contents and 7.7 name entities and you can see there also 1 million images,  test all definitions and so on and so forth.  So the first thing, the best thing to see how Babelnet works is just going into Babelnet.  So if we go to Babelnet we can see that from Miguel de Cervantes for instance I could get  a translation from Miguel de Cervantes Avedra in all these languages.  So these resources already exist and we can find in this resource information about Miguel  de Cervantes which is Miguel is an author, a Spanish writer, he was a Spanish and the  main works were Don Quixote de la Mancha for instance.  We can also see some images about Miguel de Cervantes and the most important part now  is that we can get information about Miguel de Cervantes and you can see how many languages.  So how many languages and also knowing in which language is each translation.  So going back to the presentation, so in the linguistic linked license data, so we are  talking about lezikons like Babelnet, okay, corpora, monolingual, bilingual dictionaries.  We are representing this information in RDF using a model named lemon that has been proposed  by the W3C consortium and lemon is for representing lezikons and also we are proposing using  the NLP interchange format for representing corpora.  A license is important because some resources could be opened and some resources could be  closed and we have also the open digital right language also proposed or being proposed by  the W3C for representative licensing information in a machine readable way.  So how do we represent this linked data which is behind Babelnet and other resources.  So the first thing or the easiest thing is that to represent in the traditional way using  the annotation properties in RDF.  So in that particular case if I take the identifier of Miguel de Cervantes at the Spanish National  Library the labels in different languages could be represented in this way.  But if I want to use richer models to represent linguistic information for more demanding  application like let's say for instance natural language generation I need to use lemon.  Because Miguel de Cervantes is an author and is the author of Don Quixote in that case  what we are representing in this RDF is the property author of and you can see that the  property author of has two lecicalization to representation in Spanish.  One is author there in the case of the masculine or in the case of feminine we could say is  author.  So let's say although the relation between the author and the work is author of I mean  if I'm trying to use this property for other issues in the Spanish I need to do I need to  deal with the gender.  So in that case the linguistic data and the knowledge represented in lemon could help  us to represent this information.  But also lemon can help us to represent translations in the sense that if I have for instance a  lecicon in Russian and the form of Miguel de Cervantes in Russian is in this way and  I have also the lecicon in Spanish and the written representation for Miguel de Cervantes  in this way I could link let's say these translations in lemon using some basic extensions that  has been done and built in Lithuania and also consensuated at W3C.  So let's say we can go beyond the annotation properties and labels and we can use more  complex models if we want to prepare our link data application for other type of purposes  and to make them at some point multilingual.  The other important part is that we also need to add language information to the dataset  description.  I mean if we don't say either in void or in the cat in which language is the dataset a  program need to discover the language and this is not difficult and it is a matter of  introducing this RDF in these links into the dataset description.  So at the end what we have is that we have the cloud the normal cloud the open data cloud  and we also have the linguistic cloud and as you can see in this slide we are dealing  again with vocabulary domain vocabulary for the link open data cloud, lemon, ontolex,  nif for the linguistic link data cloud but the crucial thing is that and the important  thing is that we have the same language because everything is represented in RDF and RDFS  we have also the same query language Sparkle and we have also standardized non proprietary  APIs for dealing with the domain data and also with the linguistic data and of course  linguistic data link data is linked with other resources.  So let's go now to the Spanish National Library portal and let's work with Miguel de Cervantes  and what the portal is provided everything at this moment is in Spanish so you can feel  the multilinguality issues that many non English speakers we deal with sometimes.  Okay so we are collecting information from Wikipedia we are presenting the books the  works from Miguel de Cervantes according to their importance so the first one is Don Quixote  de la Mancha and the second one is Novellas and Hemplares so an image means that we click  there we can retrieve the PDF for of this book and we also have the works about Miguel  de Cervantes okay but let's go into this part so this in this information we can get the  information of Miguel de Cervantes in different languages so now I could search in Arabic  and now I would retrieve information about the Miguel de Cervantes so at this moment  the search is prepared in order to allow the I mean different visitors to deal with querying  their own language so now of course I mean I don't in order to get the books so we need  this needs more more improvement we are working on that but let's say now if we go into Miguel  de Cervantes Savedram and we will see and I will make a query  so I will make a query about Don Quixote de la Mancha in order to show you how we have  done I mean how to use it how to use it so I don't know if this is a matter of the library  of there are many people now trying to get connected to the Spanish National Library through  internet but please I would appreciate if you don't use internet because otherwise we  I will take some time in the presentation so now I can retrieve the Spanish one of the  Spanish book or I could retrieve the books in German  it is quicker you can believe me and now I can retrieve you can see all the books that  the Spanish National Library has written in German about Don Quixote de la Mancha I will  click in this one that provides the digitalization I need to go to the to the digital library  and now in two or three clicks you can see immediately that you can start reading Don  Quixote de la Mancha in in in German okay and this is thanks to this kind of language  features that Daniel Villa has implemented in a very efficient way okay so so now we  we can expand the information if we have language information on the metadata we can expand  the information based on all language so let's see if we go to our internet you can see several  translations for for for La Cité Antique and we can see the Cité Antique is the ancient city okay  so we can get several translations that are not available in the Spanish National Library  or in the Spanish National Library and with this information from BubbleNet we can get  the same information about the the author of this book in in so we can go to the DVD and to  search about the the author if I go to the Spanish National Library just looking for you that is  and T was which is there we can see here that we retrieve a book La Cité Antique which is in  in French okay and then we can go to the French National Library and from there we can retrieve  the digital the digitalized resource and then we could start reading in French because this book  is not digitalized in the Spanish National Library okay so by by using BubbleNet I can I can also  retrieve information in in other in other languages so so at the end we are moving forward  towards a multilingual content aggregation of scenario where data a metadata coming from  from different sites can be integrated so at this moment we have this multilingual information  which is complementary but but we don't we are not providing the services so just by by including  the the language information in the in the in different data sets I mean we are closer to this  kind of application and aggregation so the linguistics data also helps with alternative  names and and titles so for instance if I go to the if we go to the Spanish National Library and  we looked for United United Nations you can see that the first book that we retrieve is the  Naciones Unidas which is so so so the entity the first entity is Naciones Unidas because this is  the way that we name it in Spanish and also because the Spanish National Library has many books  about Naciones Unidas and there we can see more information and you can see in the fifth position  we get or in the fourth and fifth position we get the United Nations book and books if we if we make  the query based on an acronym which is ONU in Spanish ONU is the acronym for Organización de  Naciones Unidas which is the the name in the in the Spanish then we can retrieve entity we can retrieve  information about Naciones Unidas about the entity or we could get books related with the United  Nations and why is that because we have again behind some kind of linguistic data so there we are  expressing in RDF and in lemon again the acronym UN is an acronym it has a canonical form and but  the labels are UN ONU in Spanish and this canonical form it has a written representation which are  these two in the same way I could say that they have United Nations which is a multi-word expression  has a canonical form has an abbreviation which is UN and also has a label which is United Nations  in English and in this canonical form I represented the United Nations in English and Spanish and I  could add many other names so so so you can see that I'm using the in that case lemon for representing  this this information so thanks to this code we can retrieve the and we can query with alternative  names also and so the let's go now to move into provenance so provenance is really important in  order to provide metadata information about when the resource was created how the resource was  created and so on so forth so we have some some standards proposed by W3C which is problem in  order to represent the process-centric provenance so let's say in that case if if I have a particular  file and I could go that code could go through some process then I can generate a new file let's  say version one of these of this file and we also have the resource provenance in order to to to  include the description the creator the creation date the rights of a particular file and we have  their the Provo, Premise, European Analytics Model, Dublin Core and so on so forth and we also have  metadata provenance which is provenance about the provenance already introduced by by W3C so so  let's see with this with an example so in if in that particular case I'm representing the March  21 dataset of the Spanish National Library that belongs to the Spanish National Library which  is has that has been defined as an agent in the Provo ontology I also have the creation date and  the creation date and time and also we have the license I also have information about the  conversion process when it started when ended and which tools or software were using the  transformation process so in that case just because we are transforming March 21 records into our  DFS using using Marinebag which is a tool developed by by Daniel Villasueva so in that  particular case so I am writing that the the the conversion process was done using Marinebag which  is an agent which is again an agent in the Provo in the Provo ontology and and also I have the  the license for for for this dataset and of course we have at the end the metadata provenance  saying that all this process is attributed to the Spanish National Library okay so now if I  represent in our DFS I can also query in a sparkle the provenance information so another important  part is that the the license information I mean let's say the the in in data I mean not all the  data is open we can also deal with closed data and even though we can combine open data and  closed data and if we want to combine open data and closed data we need to to have the license  information also written in in in our DFS okay if we make our set the query how open is the link  open data cloud we will be surprised because in the open link data cloud all these nodes in white  means that that the license is not specified okay so I forgot to write about the license and and  also in the link open data cloud we can see some bubbles in in in orange and red which means that  the data set has some assets restrictions or that the data set is is closed so so as you can see so  the majority of the link open data cloud doesn't specify the license or some data sets appear with  a closed license which is surprising so how can we represent the license information so it will  represent the license information inside the web page this can be understood and read by by humans  but if we want machines to to to use and to process the license information we need to represent  this license in ODRL in that case the open digital rights language and there we can express who can  cannot must at what which resource and how okay so this is a model this is an ontology for  representing again the license information so we can provide and describe licenses for vocabularies  for ontologies and we can also provide licenses for for data sets so so we have licenses or we  can represent in RDF CC0 we can also represent in RDF that closed data which is published but  has not open license we can also represent linked private data this is linked data that we have  but we don't advertise and make publish to the community and we can find many many many data  sets with without the explicit license and this is a real nightmare because we can see  that we cannot we cannot use so just because of that in the framework of one Spanish project and  this work is being developed by by Victor Victor Rodrigues don't tell which is a postdoc in in  our group we we are working with RDF licensing support so we have the data model based on our  ODRL we are we have a data set called RDF license where we have the description of licenses in RDF  and we have services like Lithentius for dealing or analyzing compatibility between different type  of license and also we have tools that allows conditional access to tooling data so when we  are representing the the the license information in ODRL we we can't say or we can state for  instance that the license is defined for the whole data set for a particular graph for an ontology  for a class for a triple or even though for for a mapping and there are some actions that people  can do or cannot do they can derive translate distribute print and so on so forth there are  some parties are people that could that could allow to do the action like for instance we could say  one individual or that the the license is for a particular organization and we can also explain  some constraints saying that you can use if you attribute or you can use if you live in a particular  city or you can use in a particular time frame so in this RDF license data set we have already  represented 100 almost 150 licenses they have permanent URIs so I really recommend to you to  point to this data set and and in order to to express in RDF the the the license and if you  want to read more I mean there is a paper over there that you can read and of course we will  be glad to help with this with this topic so let's see so if we want to represent represent a  creative common a license in that case a license that has no party and no constraint this would  be the code the the RDF code for for that so this is the permanent URI for for for the license  and there you can see the the permissions of distribution derivative works and reproduction  on and it is constrained to the duty of making clear the the the attribution for instance so  how do I use this license in my data set it's very simple the only thing you have to write is my  data set license and in that case to include the URI of the the license okay so I can be and I can  make more complex things in the sense that the reproduction of a data set is limited until the  end of the year after paying 15 euros so there I have my data set I give permission to reproduce  only if you pay I pay 15 euros and there at the end you can see the constraint until the end of  the the year so I can also express licenses for classes and and context and this can open new  business models for for for the use of library link data so I could say okay all book are offered  for free new books will be sold for five euros so so in that in that case you can see that for for  the the the class or book every I can do any kind of action and reproduce and also but however  for the new books I can do the action only if I pay the five the five euros and also I can express  more complex a question license is like you can't pay the whole 50 euros for the whole data set or  I can't pay 0.01 euro for instance if I want to get that trip okay so the granularity the level of  granularity and the tail without the URL is really impressive if we are using this this  ontology so this model that we have already built so at the end what we have again is that we have  the cloud we have the linguistic cloud we have the license represented in in in our gf so everything  is in our df everything is in queries could be in a sparkle open api's and non-propellery api's  and also linking this information with with other parts so okay so now I will forget now about I  will go back now to the second part of the talk which is the process being followed for for for  generating library link data and I will take a sample from the from that was from the Spanish  National Library so there is a journal paper which is already there that you can read if you want to  see more more the text the the first thing to say is that the ontology engineering group and the  Spanish National Library is a team a team working composed by by cataloging services people working  cataloging services in the digital library and researchers working in the ontology engineering  group so so thanks to to the excellent collaboration between these two institutions we have this  Spanish National Library portal the the methodology that we have followed is the classical one in  building linked data applications specification modeling rdf generation data linking publication  and exploitation and data curation is mainly mainly done at the beginning during the specification  modeling and an rdf generation this is an experience from other domains where the process of linking  data helps to improve the quality of the data in the in the in the original in the original source  so when when just some some data about the the Spanish National Library data sets so the records  were in March 21 format we had 4.5 million bibliographical records 4.5 million authority  records and this was the version the last version we took in in September 2015 so as I said before  the we wanted to have the the the team from the Spanish National Library in board the catalog is  from the very beginning Ricardo Ana Mar and and also we wanted to make the we wanted to include  the multilingual aspects from the very beginning and we started in the first round the collaboration  with with Ibla so so so there in this slide you can see the that everything has been transformed  into into rdf using marimba and marimba also links with other library data sources and also with  dvp so for for modeling the the ontology in the Spanish National Library we we have done it in  two phases in the first phase we reuse the urize from the standard vocabularies mainly Ibla fever  frat and now in the second version of the ontology portal we integrated we built an ontology which  is the b&e ontology and that reuse and links the terminologies from the from the from other  resources why why did we build this ontology because we wanted to provide stability in the use of  the the ontology and the the data that be a b&e domain the second is that we can document the the  ontology in a central document we can enrich and provide new properties and and relate and  relationships for instance those that are related with the Spanish legal deposit we can we can have  the control over the description and and labels in particular for for visualization we could deal  with all these multilingual aspects because the the ontology is described in English and  Spanish and also to allow content negotiation for to be used by different type of API so if you  want to go to the and to to see the ontology it is available under this domain so this was the  first version of the the ontology where we reuse many classes from from fever so now we have the  the ontology which is you can see it is the same but just persisted in the in the in the Spanish  National Library at least in this high-level view so in order to promote and to explode  alternatives for for names and and touch on titles so what we have done is in to deal with  investing alternatives mainly related with acronyms and and translation so for instance  for person we have the authority name and we also have the variant names for for work we have  the the authority title and the variant titles and for organization we have the authority name  and the variant names so this is dealt with as I some of these things are built as I mentioned  before for the G so you can imagine so the power of the the the power of the ontology is later on  transferring to the the user interface because the user interface is using many of the relationships  that we have in the in the in the ontology so the third step is the generation of of the the  mappings or sorry the generation in RDF so from these mark 21 records we need to to establish  how this mark 21 records will be transformed into the the ontologies that that a particular  author is a is a person and for that our friends from the Spanish National Library built the the  mapping and then Marine Bap processes the mapping and generated the the RDF code instantiated the  the the the ontology for the links we identify where were the which one where the data the data to  be linked with and possibly you have already seen this is light many many times so we created  many same as links with the other libraries it should be good if at some point all the libraries  start linking together and reversing the the same as links and for publication we have used this  is something new we have combined the cat and and void in order to include the licensing  formation provenance information with provok language information using lesbo and also allowing  three access mechanics in particular a sparkling point and data dumps so the data that the any  that is a is a catalog and it is composed of seven data sets with different distributions and and  you can't see also the the profile in these in this way so so they are here you can see the  information the decad graph with information about the seven data sets the catalog distribution and  so on so let's go so in this part you can see that this is the way that we have been using for  representing the licensing formation so there we will have a we have a link to the RDF we  already have a link to the RDF license data set we also have the description in one almost 200  language using lesbo we define the BNE as a publisher in the catalog as an agent so if we  click in the BNE we go into the web page of the that is when a with that described the  biblioteca nacional de spain with different books and works and so on so forth and and also you  can see here that the the catalog contains seven data sets and this allows us also to to help a lot  in the in the when building the new interface because now we can't provide better access if  people are looking for people entity works and and things okay so and this is the way of the  distribution in and also the to describe the different licenses also at the level of the the  data set okay so and at the end the exploitation through the portal website and services so the  for exploitation we we we have created a waiting function that uses the the ontology and also  the incoming and oncoming outgoing links from the from the notes in the ontology that and we use  also from the data that we have in the sparkling point also a string similarity in order to rank  so if you start writing the name of a of an author so immediately the first one will be the the  most famous one or with more books and works in in the Spanish National Library so for instance if  I start with Lopez so in that case there is a big difference between the first one which is Lopez  de Vega which is a famous writer and the second one is also Lopez de Rueda which is another one okay  so so now the results of the of the social you can see here the the volumetry so in total we have  four as I mentioned before for 4.5 million records of authority by biographics we have this  number of triples more than a one million links with the Biaf Sudok the German National Library  Libris the Wikipedia the French National Library and also with Geolink data Geolink data is a link  data application based on the geographical domain okay how can we use the library link  metadata of the library link linked data sorry I forgot to translate so the the the first one is  to validate and and and and reach registry that are in March 21 records in other Spanish national  in other Spanish libraries that are part of the long tail of of libraries okay so this is this is  one of the uses because we have metadata curated I mean all this metadata which is created in  Spanish National Library can be helped can be used for for for enriching other 20 mark 21 records  in other library so so this is an example of the long tail which is a unespecialized forum for rare  editions so you can see one famous Spanish singer from the south singing flamenco and this kind of  things and and there you can see that that the person wrote okay just looking at the Spanish National  Library I found works from this person that I didn't I didn't know we also found newspapers that are  also pointing to to some information from the Spanish National Library metadata another use is  something related with citizen science in that particular case we we built a prototype of an  application named a street detective and the goal was to improve data quality and access to  cultural data in in in a particular city so what we did was to build an application that  have the Zaragoza map on the back and and also we have the Spanish National Library portal and  Oscar Kortso one of the the vice director of the group I mean he made a challenge with kids and  children and and then by using games I mean the children between 10 and 15 years old they were  just linking are based on some questions they were linking streets with the authors of the Spanish  National Library okay so so in order to have information on the map linked with the with  Spanish National Library and an Oscar Kortso got a Fujitsu price this year just because of this  application so imagine now just based on this application we can build a more complex case  where we can integrate cultural data and geographical data and this is the the a neighborhood in  Madrid where many of the streets have and names of famous Spanish writer writers so so I could  get information about this neighborhood just going into Wikipedia I could get information about  this museum just going into Wikipedia but if I search in the Spanish National Library about  Museo Lope de Vega immediately I could retrieve some books about that or the same for Ateneo de  Madrid with pictures images and so on so forth or for the author of Lope de Vega or the same for  the street of Tervantes so in that case I would navigate instead of using wiki web pages using  the web of data the same if I want El Quixote road this is the road in in in Google Maps but if I  search by Ruta Don Quixote which is the Spanish translation I would get several books about that  tell me about about the route and and if I want to search locations related with El Quixote in that  case I have El Tavoso over over here and I have Alcázar de San Juan so I could get information  about that or I made a mistake with the animation or if I could search by Tavoso Don Quixote and  Tavoso Dulpina and then I would retrieve information about books writing about El Tavoso but under a  particular context which is Don Quixote or Dulpina I can also based on the metadata just from the  metadata in images to retrieve some full test in the from the National Library because the some  PDFs are a viral okay so which one is the message to take home that a library in data can be  easily integrated in in other with all the data in different type of applications so two reams geography  education are the first one to explore data provides providers should include language provenance  and license metadata in their data sets not only in the March 21 records that of course you do but  also when you generate into the the the RDF and finally it's important to say that the benefits  of adding language provenance and license metadata in the data set reduce the time and cost of  identify the language in resources and terminology and allows you to point to two other terminologies  it also it also fostered the aggregation and enrichment of data across complementary domains  you have already seen some examples it also has the data curation because you can assess your data  against other data to see if you are providing the same information and also improve the precision  and recall in information retrieval and search so thank you very much and I'm available for any  questions that you already have
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Okay, yes, as Jojen already said, this is a work that has been done in collaboration with the Spanish National Library and in particular with a great development realized by Daniel Villasuedo who is now in Madrid, continuing with the developments. So the motivation of, so I will start this talk with motivation and making clear how to deal with other kinds of metadata that are related with language, provenance and license which are extremely useful when trying to use a library link and metadata in final application. I will also review the link data process followed with examples from the Spanish National Library and at the end I will highlight some uses of the library link metadata in other fields and how library metadata can be integrated, can be reused with link data coming from the geographical and education domain. So we live at this moment in a world of digital data, so we have many providers, city halls, governments, devices, also people generating metadata and a lot of data being generated in social media. And this metadata is available and this data in different domains, governmental energy, library, geography and also it appears in different formats. It is not only the March 21 records that you are used to work with but also we have data in formats, databases, geographical data, in a special format given by sensors and also in link data. And the most important part is that the data is language oriented in the sense that we are generating thousands of link data, datasets in the cloud for other purposes and in many of the languages and not all the data that we have is open. At some time we need to work with closed data. So we live in an ecosystem of resources already in different silos that belongs to different complementary domains that are in different formats, in different languages and store in repositories with different metadata. And of course the life of this program is really hard when trying to use many API and services for querying the metadata available. So the data sources that we are using are complementary but at some point they are not connected. So the good things of link data is that it allows the uniform access to the data that has been transformed into RDF. When working application with based on link data we need to agree on vocabularies for describing the metadata and also the domain data. We have a language and a standardized language proposed by W3C RDFS for writing down the models and also RDF for the data. We have Sparkle as a standard query language and we have many standardized non-properitary APIs for using the data that has been made available. But the power of link data you already know is the links that we have with other resources and these links could be to data in the same language or data that appear in different languages. But for the end user that is trying to use the link data format, quality is something very important and before starting using data sets that are available there are many questions. Related with who generated the data set, how the data set was transformed, when the data set was created in order to see if it is providing updated data. Is that the last version of the data set? Which one is the license of the data set? Is the license clearly stated in the data set? In some graphs? In some triples? In which formats the data are delivered and if data are monolingual or multilingual? So at the end in order to discover the quality of a particular data set, the metadata really matters. I mean metadata matters because metadata about data could express provenance information, could express license information, could express language information, privacy if we are dealing with data related to people. And of course there are other types of metadata related with geolocation, spatial time that needs to be represented. So mainly in this talk what I am going to present in the first part is how to represent metadata related with language, provenance and licenses. And how this metadata could be used later on for developing some kind of applications or in some new cases. So as Joao mentioned before, I mean I am coordinating a project named Leader which is about linguistic link data. And we already know that link data is multilingual and we know that the library is stored multilingual data. So let's say if you go to VIA you can see here Miguel de Cervantes Saavedra, one of the famous writers from Spanish literature in many different languages. If we go to the Spanish National Library we also have some variants for representing Miguel de Cervantes in different languages. In that case you can see that we are not saying in which language is each variant, in the case of VIA it appears because of the flags. One in the French National Library we can take Don Quixote which is one of the famous books written by Cervantes and there we can see the translation of Don Quixote into different languages for French and Spanish. It is the language but for the other titles we need to discover which one is the language. And of course we have also the resources like the Vipidia which are providing links from other data sets where we can find information about Miguel de Cervantes in different languages. So when we are talking about linguistic link data, so these are examples that show that we really need to deal with language. So several years ago, two years ago, three years ago we started to think about how to represent language information in link data and how linguistic information could help in order to deal with language. So we came to this idea of the linguistic link data cloud. Three years ago it was a dream, now it is a reality because we have more than 150 resources on the linguistic link data cloud and the linguistic link data cloud is a subset of the link open data cloud. Just focus on the linguistic domain with many types of resources because there we can find corpora, terminological databases, linguistic data categories, typological databases, lezikons and also bilingual dictionaries or monolingual dictionaries. So in this linguistic link data cloud, language resources are connected and are connected with other language resources. And this is very important because linguistic link data will allow or is allowing at this moment the interconnection of words and linguistic information that appear in different languages and also it enables the leziklicization of data on the web. And that could be in the link data format or that could be in other formats. And it can also help with the multilingual search. And so let's say, so the linguistic link data could help to translate the same term, a book title, an author name, a place in different languages and could also help to deal with acronyms. And we'll see later on how we have used these linguistic link data for building the Spanish National Library portal. So this is the core of the linguistic link open data cloud. There are resources like the Wikipedia. We also have resources like the Pabellet. We also have resources like Lezinfo. So this part of the cloud is full of the aperitif dictionaries just translating or representing the RDF dictionaries that are between Spanish and French, Spanish and English, Catalan, and Italian and so on and so forth. So what is Babelnet? Babelnet was built by Roberto Naviglia, the Sapienza University, and it is a multilingual lezikon that has been built by merging resources of different kinds. So Babelnet has been used using Wordnet, Wikipedia, Wikidata, Omega Wiki and so on and so forth. So the same content and the same entity in Babelnet is expressed in tens of languages. So right now version 3.5 of Babelnet already has covered 272 languages and more than 14 million entries. So 6 million contents and 7.7 name entities and you can see there also 1 million images, test all definitions and so on and so forth. So the first thing, the best thing to see how Babelnet works is just going into Babelnet. So if we go to Babelnet we can see that from Miguel de Cervantes for instance I could get a translation from Miguel de Cervantes Avedra in all these languages. So these resources already exist and we can find in this resource information about Miguel de Cervantes which is Miguel is an author, a Spanish writer, he was a Spanish and the main works were Don Quixote de la Mancha for instance. We can also see some images about Miguel de Cervantes and the most important part now is that we can get information about Miguel de Cervantes and you can see how many languages. So how many languages and also knowing in which language is each translation. So going back to the presentation, so in the linguistic linked license data, so we are talking about lezikons like Babelnet, okay, corpora, monolingual, bilingual dictionaries. We are representing this information in RDF using a model named lemon that has been proposed by the W3C consortium and lemon is for representing lezikons and also we are proposing using the NLP interchange format for representing corpora. A license is important because some resources could be opened and some resources could be closed and we have also the open digital right language also proposed or being proposed by the W3C for representative licensing information in a machine readable way. So how do we represent this linked data which is behind Babelnet and other resources. So the first thing or the easiest thing is that to represent in the traditional way using the annotation properties in RDF. So in that particular case if I take the identifier of Miguel de Cervantes at the Spanish National Library the labels in different languages could be represented in this way. But if I want to use richer models to represent linguistic information for more demanding application like let's say for instance natural language generation I need to use lemon. Because Miguel de Cervantes is an author and is the author of Don Quixote in that case what we are representing in this RDF is the property author of and you can see that the property author of has two lecicalization to representation in Spanish. One is author there in the case of the masculine or in the case of feminine we could say is author. So let's say although the relation between the author and the work is author of I mean if I'm trying to use this property for other issues in the Spanish I need to do I need to deal with the gender. So in that case the linguistic data and the knowledge represented in lemon could help us to represent this information. But also lemon can help us to represent translations in the sense that if I have for instance a lecicon in Russian and the form of Miguel de Cervantes in Russian is in this way and I have also the lecicon in Spanish and the written representation for Miguel de Cervantes in this way I could link let's say these translations in lemon using some basic extensions that has been done and built in Lithuania and also consensuated at W3C. So let's say we can go beyond the annotation properties and labels and we can use more complex models if we want to prepare our link data application for other type of purposes and to make them at some point multilingual. The other important part is that we also need to add language information to the dataset description. I mean if we don't say either in void or in the cat in which language is the dataset a program need to discover the language and this is not difficult and it is a matter of introducing this RDF in these links into the dataset description. So at the end what we have is that we have the cloud the normal cloud the open data cloud and we also have the linguistic cloud and as you can see in this slide we are dealing again with vocabulary domain vocabulary for the link open data cloud, lemon, ontolex, nif for the linguistic link data cloud but the crucial thing is that and the important thing is that we have the same language because everything is represented in RDF and RDFS we have also the same query language Sparkle and we have also standardized non proprietary APIs for dealing with the domain data and also with the linguistic data and of course linguistic data link data is linked with other resources. So let's go now to the Spanish National Library portal and let's work with Miguel de Cervantes and what the portal is provided everything at this moment is in Spanish so you can feel the multilinguality issues that many non English speakers we deal with sometimes. Okay so we are collecting information from Wikipedia we are presenting the books the works from Miguel de Cervantes according to their importance so the first one is Don Quixote de la Mancha and the second one is Novellas and Hemplares so an image means that we click there we can retrieve the PDF for of this book and we also have the works about Miguel de Cervantes okay but let's go into this part so this in this information we can get the information of Miguel de Cervantes in different languages so now I could search in Arabic and now I would retrieve information about the Miguel de Cervantes so at this moment the search is prepared in order to allow the I mean different visitors to deal with querying their own language so now of course I mean I don't in order to get the books so we need this needs more more improvement we are working on that but let's say now if we go into Miguel de Cervantes Savedram and we will see and I will make a query so I will make a query about Don Quixote de la Mancha in order to show you how we have done I mean how to use it how to use it so I don't know if this is a matter of the library of there are many people now trying to get connected to the Spanish National Library through internet but please I would appreciate if you don't use internet because otherwise we I will take some time in the presentation so now I can retrieve the Spanish one of the Spanish book or I could retrieve the books in German it is quicker you can believe me and now I can retrieve you can see all the books that the Spanish National Library has written in German about Don Quixote de la Mancha I will click in this one that provides the digitalization I need to go to the to the digital library and now in two or three clicks you can see immediately that you can start reading Don Quixote de la Mancha in in in German okay and this is thanks to this kind of language features that Daniel Villa has implemented in a very efficient way okay so so now we we can expand the information if we have language information on the metadata we can expand the information based on all language so let's see if we go to our internet you can see several translations for for for La Cité Antique and we can see the Cité Antique is the ancient city okay so we can get several translations that are not available in the Spanish National Library or in the Spanish National Library and with this information from BubbleNet we can get the same information about the the author of this book in in so we can go to the DVD and to search about the the author if I go to the Spanish National Library just looking for you that is and T was which is there we can see here that we retrieve a book La Cité Antique which is in in French okay and then we can go to the French National Library and from there we can retrieve the digital the digitalized resource and then we could start reading in French because this book is not digitalized in the Spanish National Library okay so by by using BubbleNet I can I can also retrieve information in in other in other languages so so at the end we are moving forward towards a multilingual content aggregation of scenario where data a metadata coming from from different sites can be integrated so at this moment we have this multilingual information which is complementary but but we don't we are not providing the services so just by by including the the language information in the in the in different data sets I mean we are closer to this kind of application and aggregation so the linguistics data also helps with alternative names and and titles so for instance if I go to the if we go to the Spanish National Library and we looked for United United Nations you can see that the first book that we retrieve is the Naciones Unidas which is so so so the entity the first entity is Naciones Unidas because this is the way that we name it in Spanish and also because the Spanish National Library has many books about Naciones Unidas and there we can see more information and you can see in the fifth position we get or in the fourth and fifth position we get the United Nations book and books if we if we make the query based on an acronym which is ONU in Spanish ONU is the acronym for Organización de Naciones Unidas which is the the name in the in the Spanish then we can retrieve entity we can retrieve information about Naciones Unidas about the entity or we could get books related with the United Nations and why is that because we have again behind some kind of linguistic data so there we are expressing in RDF and in lemon again the acronym UN is an acronym it has a canonical form and but the labels are UN ONU in Spanish and this canonical form it has a written representation which are these two in the same way I could say that they have United Nations which is a multi-word expression has a canonical form has an abbreviation which is UN and also has a label which is United Nations in English and in this canonical form I represented the United Nations in English and Spanish and I could add many other names so so so you can see that I'm using the in that case lemon for representing this this information so thanks to this code we can retrieve the and we can query with alternative names also and so the let's go now to move into provenance so provenance is really important in order to provide metadata information about when the resource was created how the resource was created and so on so forth so we have some some standards proposed by W3C which is problem in order to represent the process-centric provenance so let's say in that case if if I have a particular file and I could go that code could go through some process then I can generate a new file let's say version one of these of this file and we also have the resource provenance in order to to to include the description the creator the creation date the rights of a particular file and we have their the Provo, Premise, European Analytics Model, Dublin Core and so on so forth and we also have metadata provenance which is provenance about the provenance already introduced by by W3C so so let's see with this with an example so in if in that particular case I'm representing the March 21 dataset of the Spanish National Library that belongs to the Spanish National Library which is has that has been defined as an agent in the Provo ontology I also have the creation date and the creation date and time and also we have the license I also have information about the conversion process when it started when ended and which tools or software were using the transformation process so in that case just because we are transforming March 21 records into our DFS using using Marinebag which is a tool developed by by Daniel Villasueva so in that particular case so I am writing that the the the conversion process was done using Marinebag which is an agent which is again an agent in the Provo in the Provo ontology and and also I have the the license for for for this dataset and of course we have at the end the metadata provenance saying that all this process is attributed to the Spanish National Library okay so now if I represent in our DFS I can also query in a sparkle the provenance information so another important part is that the the license information I mean let's say the the in in data I mean not all the data is open we can also deal with closed data and even though we can combine open data and closed data and if we want to combine open data and closed data we need to to have the license information also written in in in our DFS okay if we make our set the query how open is the link open data cloud we will be surprised because in the open link data cloud all these nodes in white means that that the license is not specified okay so I forgot to write about the license and and also in the link open data cloud we can see some bubbles in in in orange and red which means that the data set has some assets restrictions or that the data set is is closed so so as you can see so the majority of the link open data cloud doesn't specify the license or some data sets appear with a closed license which is surprising so how can we represent the license information so it will represent the license information inside the web page this can be understood and read by by humans but if we want machines to to to use and to process the license information we need to represent this license in ODRL in that case the open digital rights language and there we can express who can cannot must at what which resource and how okay so this is a model this is an ontology for representing again the license information so we can provide and describe licenses for vocabularies for ontologies and we can also provide licenses for for data sets so so we have licenses or we can represent in RDF CC0 we can also represent in RDF that closed data which is published but has not open license we can also represent linked private data this is linked data that we have but we don't advertise and make publish to the community and we can find many many many data sets with without the explicit license and this is a real nightmare because we can see that we cannot we cannot use so just because of that in the framework of one Spanish project and this work is being developed by by Victor Victor Rodrigues don't tell which is a postdoc in in our group we we are working with RDF licensing support so we have the data model based on our ODRL we are we have a data set called RDF license where we have the description of licenses in RDF and we have services like Lithentius for dealing or analyzing compatibility between different type of license and also we have tools that allows conditional access to tooling data so when we are representing the the the license information in ODRL we we can't say or we can state for instance that the license is defined for the whole data set for a particular graph for an ontology for a class for a triple or even though for for a mapping and there are some actions that people can do or cannot do they can derive translate distribute print and so on so forth there are some parties are people that could that could allow to do the action like for instance we could say one individual or that the the license is for a particular organization and we can also explain some constraints saying that you can use if you attribute or you can use if you live in a particular city or you can use in a particular time frame so in this RDF license data set we have already represented 100 almost 150 licenses they have permanent URIs so I really recommend to you to point to this data set and and in order to to express in RDF the the the license and if you want to read more I mean there is a paper over there that you can read and of course we will be glad to help with this with this topic so let's see so if we want to represent represent a creative common a license in that case a license that has no party and no constraint this would be the code the the RDF code for for that so this is the permanent URI for for for the license and there you can see the the permissions of distribution derivative works and reproduction on and it is constrained to the duty of making clear the the the attribution for instance so how do I use this license in my data set it's very simple the only thing you have to write is my data set license and in that case to include the URI of the the license okay so I can be and I can make more complex things in the sense that the reproduction of a data set is limited until the end of the year after paying 15 euros so there I have my data set I give permission to reproduce only if you pay I pay 15 euros and there at the end you can see the constraint until the end of the the year so I can also express licenses for classes and and context and this can open new business models for for for the use of library link data so I could say okay all book are offered for free new books will be sold for five euros so so in that in that case you can see that for for the the the class or book every I can do any kind of action and reproduce and also but however for the new books I can do the action only if I pay the five the five euros and also I can express more complex a question license is like you can't pay the whole 50 euros for the whole data set or I can't pay 0.01 euro for instance if I want to get that trip okay so the granularity the level of granularity and the tail without the URL is really impressive if we are using this this ontology so this model that we have already built so at the end what we have again is that we have the cloud we have the linguistic cloud we have the license represented in in in our gf so everything is in our df everything is in queries could be in a sparkle open api's and non-propellery api's and also linking this information with with other parts so okay so now I will forget now about I will go back now to the second part of the talk which is the process being followed for for for generating library link data and I will take a sample from the from that was from the Spanish National Library so there is a journal paper which is already there that you can read if you want to see more more the text the the first thing to say is that the ontology engineering group and the Spanish National Library is a team a team working composed by by cataloging services people working cataloging services in the digital library and researchers working in the ontology engineering group so so thanks to to the excellent collaboration between these two institutions we have this Spanish National Library portal the the methodology that we have followed is the classical one in building linked data applications specification modeling rdf generation data linking publication and exploitation and data curation is mainly mainly done at the beginning during the specification modeling and an rdf generation this is an experience from other domains where the process of linking data helps to improve the quality of the data in the in the in the original in the original source so when when just some some data about the the Spanish National Library data sets so the records were in March 21 format we had 4.5 million bibliographical records 4.5 million authority records and this was the version the last version we took in in September 2015 so as I said before the we wanted to have the the the team from the Spanish National Library in board the catalog is from the very beginning Ricardo Ana Mar and and also we wanted to make the we wanted to include the multilingual aspects from the very beginning and we started in the first round the collaboration with with Ibla so so so there in this slide you can see the that everything has been transformed into into rdf using marimba and marimba also links with other library data sources and also with dvp so for for modeling the the ontology in the Spanish National Library we we have done it in two phases in the first phase we reuse the urize from the standard vocabularies mainly Ibla fever frat and now in the second version of the ontology portal we integrated we built an ontology which is the b&e ontology and that reuse and links the terminologies from the from the from other resources why why did we build this ontology because we wanted to provide stability in the use of the the ontology and the the data that be a b&e domain the second is that we can document the the ontology in a central document we can enrich and provide new properties and and relate and relationships for instance those that are related with the Spanish legal deposit we can we can have the control over the description and and labels in particular for for visualization we could deal with all these multilingual aspects because the the ontology is described in English and Spanish and also to allow content negotiation for to be used by different type of API so if you want to go to the and to to see the ontology it is available under this domain so this was the first version of the the ontology where we reuse many classes from from fever so now we have the the ontology which is you can see it is the same but just persisted in the in the in the Spanish National Library at least in this high-level view so in order to promote and to explode alternatives for for names and and touch on titles so what we have done is in to deal with investing alternatives mainly related with acronyms and and translation so for instance for person we have the authority name and we also have the variant names for for work we have the the authority title and the variant titles and for organization we have the authority name and the variant names so this is dealt with as I some of these things are built as I mentioned before for the G so you can imagine so the power of the the the power of the ontology is later on transferring to the the user interface because the user interface is using many of the relationships that we have in the in the in the ontology so the third step is the generation of of the the mappings or sorry the generation in RDF so from these mark 21 records we need to to establish how this mark 21 records will be transformed into the the ontologies that that a particular author is a is a person and for that our friends from the Spanish National Library built the the mapping and then Marine Bap processes the mapping and generated the the RDF code instantiated the the the the ontology for the links we identify where were the which one where the data the data to be linked with and possibly you have already seen this is light many many times so we created many same as links with the other libraries it should be good if at some point all the libraries start linking together and reversing the the same as links and for publication we have used this is something new we have combined the cat and and void in order to include the licensing formation provenance information with provok language information using lesbo and also allowing three access mechanics in particular a sparkling point and data dumps so the data that the any that is a is a catalog and it is composed of seven data sets with different distributions and and you can't see also the the profile in these in this way so so they are here you can see the information the decad graph with information about the seven data sets the catalog distribution and so on so let's go so in this part you can see that this is the way that we have been using for representing the licensing formation so there we will have a we have a link to the RDF we already have a link to the RDF license data set we also have the description in one almost 200 language using lesbo we define the BNE as a publisher in the catalog as an agent so if we click in the BNE we go into the web page of the that is when a with that described the biblioteca nacional de spain with different books and works and so on so forth and and also you can see here that the the catalog contains seven data sets and this allows us also to to help a lot in the in the when building the new interface because now we can't provide better access if people are looking for people entity works and and things okay so and this is the way of the distribution in and also the to describe the different licenses also at the level of the the data set okay so and at the end the exploitation through the portal website and services so the for exploitation we we we have created a waiting function that uses the the ontology and also the incoming and oncoming outgoing links from the from the notes in the ontology that and we use also from the data that we have in the sparkling point also a string similarity in order to rank so if you start writing the name of a of an author so immediately the first one will be the the most famous one or with more books and works in in the Spanish National Library so for instance if I start with Lopez so in that case there is a big difference between the first one which is Lopez de Vega which is a famous writer and the second one is also Lopez de Rueda which is another one okay so so now the results of the of the social you can see here the the volumetry so in total we have four as I mentioned before for 4.5 million records of authority by biographics we have this number of triples more than a one million links with the Biaf Sudok the German National Library Libris the Wikipedia the French National Library and also with Geolink data Geolink data is a link data application based on the geographical domain okay how can we use the library link metadata of the library link linked data sorry I forgot to translate so the the the first one is to validate and and and and reach registry that are in March 21 records in other Spanish national in other Spanish libraries that are part of the long tail of of libraries okay so this is this is one of the uses because we have metadata curated I mean all this metadata which is created in Spanish National Library can be helped can be used for for for enriching other 20 mark 21 records in other library so so this is an example of the long tail which is a unespecialized forum for rare editions so you can see one famous Spanish singer from the south singing flamenco and this kind of things and and there you can see that that the person wrote okay just looking at the Spanish National Library I found works from this person that I didn't I didn't know we also found newspapers that are also pointing to to some information from the Spanish National Library metadata another use is something related with citizen science in that particular case we we built a prototype of an application named a street detective and the goal was to improve data quality and access to cultural data in in in a particular city so what we did was to build an application that have the Zaragoza map on the back and and also we have the Spanish National Library portal and Oscar Kortso one of the the vice director of the group I mean he made a challenge with kids and children and and then by using games I mean the children between 10 and 15 years old they were just linking are based on some questions they were linking streets with the authors of the Spanish National Library okay so so in order to have information on the map linked with the with Spanish National Library and an Oscar Kortso got a Fujitsu price this year just because of this application so imagine now just based on this application we can build a more complex case where we can integrate cultural data and geographical data and this is the the a neighborhood in Madrid where many of the streets have and names of famous Spanish writer writers so so I could get information about this neighborhood just going into Wikipedia I could get information about this museum just going into Wikipedia but if I search in the Spanish National Library about Museo Lope de Vega immediately I could retrieve some books about that or the same for Ateneo de Madrid with pictures images and so on so forth or for the author of Lope de Vega or the same for the street of Tervantes so in that case I would navigate instead of using wiki web pages using the web of data the same if I want El Quixote road this is the road in in in Google Maps but if I search by Ruta Don Quixote which is the Spanish translation I would get several books about that tell me about about the route and and if I want to search locations related with El Quixote in that case I have El Tavoso over over here and I have Alcázar de San Juan so I could get information about that or I made a mistake with the animation or if I could search by Tavoso Don Quixote and Tavoso Dulpina and then I would retrieve information about books writing about El Tavoso but under a particular context which is Don Quixote or Dulpina I can also based on the metadata just from the metadata in images to retrieve some full test in the from the National Library because the some PDFs are a viral okay so which one is the message to take home that a library in data can be easily integrated in in other with all the data in different type of applications so two reams geography education are the first one to explore data provides providers should include language provenance and license metadata in their data sets not only in the March 21 records that of course you do but also when you generate into the the the RDF and finally it's important to say that the benefits of adding language provenance and license metadata in the data set reduce the time and cost of identify the language in resources and terminology and allows you to point to two other terminologies it also it also fostered the aggregation and enrichment of data across complementary domains you have already seen some examples it also has the data curation because you can assess your data against other data to see if you are providing the same information and also improve the precision and recall in information retrieval and search so thank you very much and I'm available for any questions that you already have
and at the end I will highlight some uses of the library link metadata in other fields and how thousands of link data, datasets in the cloud for other purposes and in many of the languages and in an ecosystem of resources already in different data set? In some graphs? In some triples? In are other types of metadata related with represented. So mainly in this talk what I am could be used later on for developing some kind of applications or in some new cases. So as Joao are providing links from other data sets where we different languages. So when we are talking about on the web. And that could be in the link data can also help with the multilingual search. And Library portal. So this is the core of the representing corpora. A license is important resources could be closed and we have also the in the traditional way using the annotation that case the linguistic data and the knowledge that we also need to add language information to digitalization I need to go to the to the digital this is thanks to this kind of language features so we have some some standards proposed by W3C open is the link open data cloud we will be information so it will represent the license information we need to represent this license in ODRL in that case the open digital rights the description of licenses in RDF and we have and also we have tools that allows conditional set for a particular graph for an ontology for a business models for for for the use of library classical one in building linked data generation data linking publication and exploitation and data curation is mainly mainly experience from other domains where the process library data sources and also with dvp so for for ontology and that reuse and links the with all these multilingual aspects because the go to the and to to see the ontology it is ontology so the third step is the generation of data application based on the geographical domain quality and access to cultural data in in in a the first one to explore data provides providers it's important to say that the benefits of adding