21391273
Community discovery and analysis in blogspace
Weblog has quickly evolved into a new information and knowledge dissemination channel. Yet it is not easy to discover weblog communities through keyword search. The main contribution of this paper is the study of weblog communities from the perspective of social network analysis. We proposed a new way of collecting and preparing data for weblog community discovery. The data collection stage focuses on gaining knowledge of the strength of social ties between weblogs. The strength of social ties and the clustering feature of social network guided the discovery of weblog communities.
3198534
Understanding Information Technology Complementarities Using an Augmented Endogenous Growth Framework
The complex relationships between information technology investments and organizational practices have been the focus of intensive research in recent years. The research focus appears to have shifted to investigating the effects of various organizational practices and their interactions with information technology capital. There also is emerging evidence of recent emphasis on organizational factors and a greater shift toward IT complementarities in which value addition is linked to combining complementary organizational practices with IT investments. In this paper we focus on the contributions of IT use and organizational practices and their interactions. We use firm-level data to extend previous studies in three ways: (1) by using a large sample covering 3,299 firms spanning 11 industries, (2) by focusing on a large number of organizational practices, and (3) by augmenting the endogenous growth accounting framework with a data mining technique. Our findings indicate that the set of interrelated organizational practices that complement positively to IT use is different from the set of practices hindering IT use. We conclude that IT complementary factors that positively affect productivity growth do not necessarily have a reverse effect when they are reduced or removed. It appears that some organizational factors have asymmetrical effects on growth.
4850818
Analysis of Weblog Link Structure - A Community Perspective
In this paper, we report a two level study on weblog link structures. At the micro level, we carried out an in-depth investigation of individual weblogs. Our goal was to obtain some preliminary understanding of the different types of links that might indicate underlying communities of bloggers. Complete and detailed link data was collected from eight weblogs followed by a variety of analyses. The result shows that both incoming and outgoings follow Zipf like distribution in terms of the sources of those links. These suggest clustering patterns (communities) within the whole blogspace. We also examine the temporal aspects of weblogs. The average life span of a weblog entry is fairly long in most of our sample cases. In addition, analysis on individual comment authors shows that in average, active comment authors maintain a rather long relationship with a certain weblog. It provides evidence that historical data may be useful in understanding weblog communities. On a larger scale, we developed a program to collect complete link data from large number of interconnected weblogs and performed cluster analysis on it. Communities with common topics are successfully extracted using those link data.
17316572
Aligning Ontologies through Formal Concept Analysis
Abstract.Ontologies have been developed for a number of knowledge domains as di-verseasclinicalterminology,photocamerapartsandmicro-arraygeneexpressiondata.However,aninnatecharacteristicofthedevelopmentofontologiesisthattheyareoftencreatedbyindependentgroupsofexpertise,whichgeneratedthenecessityofmergingandaligningontologiescoveringoverlappingdomains.Manyalgorithmsandtoolshavebeenproposedformergingofontologies,butmostofthemdisregardthestructuralpropertiesofthesourceontologies,focusingmostlyonsyntacticanalysis.This article focuses on an alignment method for ontologies based on Formal ConceptAnalysis,adataanalysistechniquefoundedonlatticetheory. 1 Introduction IntheheartofSemanticWebinitiative,proposedbyTimBerners-lee[2,3],liestheideaofmachineprocessableinformation,suchthatautomatedagents,orsoftbots,couldscanthewebspaceforinformationwithaspeciﬂcmeaning.Nevertheless,theunderstandingofmeaninginWebpagesdesignedforhumansisnotaneasytaskforthepresentgenerationofsoftbotsandontologiescangreatlyassistinthisendeavour.Manyinitiativesaroundtheworldhavecreatedontologies.Someoftheseontologieswerecreatedforhighleveldescriptionoftheworld,whereasothersrepresentveryspeciﬂcsub-jects,likebreastcancer,forinstance.Sometimes,ontologiesdevelopedindependentlymayhavesomeoverlaps.TheforementionedBreastCancerOntology,forexample,mayhavecommonconceptsandrelationswithaGeneOntology.Theseoverlapsareveryimpor-tantfromthepointofviewofinformationintegration,becausethecommonontologicalentitieshavetobeidentiﬂedbeforeinformationcanbesuccessfullyintegrated.Forthisreason,mergeandalignmentofontologieshavereceivedsigniﬂcantattentionfromtheresearchcommunityinthelastsixyears.Theﬂrstimportantissueinmerge/alignmentofontologiesistherelationsymbol-meaning.Ontologiesareconstitutedofsymbols(entities)andrelationsbetweensymbols.However,inSemiotics[18]symbolshaveassociatedmeaning,whichlinksthemtotheirreferent
35187915
Conceptual Data Modelling in Database Design: Similarities and Differences between Expert and Novice Designers
Abstract This paper explores the similarities and differences between experts and novices engaged in a conceptual data modelling task, a critical part of overall database design, using data gathered in the form of think-aloud protocols. It develops a three-level process model of the subjects' behavior and the differentiated application of this model by experts and novices. The study found that the experts focused on generating a holistic understanding of the problem before developing the conceptual model. They were able to categorize problem descriptions into standard abstractions. The novices tended to have more errors in their solutions largely due to their inability to integrate the various parts of the problem description and map them into appropriate knowledge structures. The study also found that the expert and novice behavior was similar in terms of modelling facets like entity, identifier, descriptor and binary relationship, somewhat different in modelling ternary relationship, but quite different in the modelling of unary relationship and category. These findings are discussed in relation to the results of previous expert-novice studies in other domains.
18286129
Workflow Based Just-in-time Training
This paper focuses on the problem of information overload for newcomers in an organisation. We propose to address it by constructing a smart personal training assistant based upon workflow tools to drive temporal management of a just-intime workplace training system which will deliver a personalised and structured presentation of organisational documents.
2049771
Discovering Dynamics Using Bayesian Clustering
This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the different dynamics. To increase efficiency, the method uses an entropy-based heuristic search strategy.
10174533
Cost-Sensitive Decision Tree Learning for Forensic Classification
In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the forensic classification problem and present a new algorithm for building decision trees that maximizes classification accuracy while minimizing total feature costs. By expressing the ID3 decision tree algorithm in an information theoretic context, we derive our algorithm from a well-formulated problem objective. We evaluate our algorithm across several datasets and show that, for a given level of accuracy, our algorithm builds cheaper trees than existing methods. Finally, we apply our algorithm to a real-world system, Clarify. Clarify classifies unknown or unexpected program errors by collecting statistics during program runtime which are then used for decision tree classification after an error has occurred. We demonstrate that if the classifier used by the Clarify system is trained with our algorithm, the computational overhead (equivalently, total feature costs) can decrease by many orders of magnitude with only a slight (<1%) reduction in classification accuracy.
1243996
Real-time closed-world tracking
A real-time tracking algorithm that uses contextual information is described. The method is capable of simultaneously tracking multiple, non-rigid objects when erratic movement and object collisions are common. A closed-world assumption is used to adaptively select and weight image features used for correspondence. Results of algorithm testing and the limitations of the method are discussed. The algorithm has been used to track children in an interactive, narrative playspace.
8652205
Copy detection mechanisms for digital documents
In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity.In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security). We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.
2738544
Estimating the global pagerank of web communities
Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains. One disadvantage such systems have over large-scale search engines is the lack of global PageRank values. Such information is needed to assess the value of pages in the localized search domain within the context of the web as a whole. In this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain. The algorithms are all highly scalable in that, given a local domain of size n, they use O(n) resources that include computation time, bandwidth, and storage. We test our methods across a variety of localized domains, including site-specific domains and topic-specific domains. We demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.