473917
Leveraging data and structure in ontology integration
There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems - the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25% in quality over FCA-merge and a 11% improvement in recall over COMA++.
5593182
Representing and querying data transformations
Modern information systems often store data that has been transformed and integrated from a variety of sources. This integration may obscure the original source semantics of data items. For many tasks, it is important to be able to determine not only where data items originated, but also why they appear in the integration as they do and through what transformation they were derived. This problem is known as data provenance. In this work, we consider data provenance at the schema and mapping level. In particular, we consider how to answer questions such as "what schema elements in the source(s) contributed to this value", or "through what transformations or mappings was this value derived?" Towards this end, we elevate schemas and mappings to first-class citizens that are stored in a repository and are associated with the actual data values. An extended query language, called MXQL, is also developed that allows meta-data to be queried as regular data and we describe its implementation scenario.
16826457
Creating Nested Mappings with Clio
Schema mappings play a central role in many data integration and data exchange scenarios. In those applications, users need to quickly and correctly specify how data represented in one format is converted into a different format. Clio (L. Popa et al., 2002) is a joint research project between IBM and the University of Toronto studying the creation, maintenance, and use of schema mappings. There have always been two goals in our work in Clio: 1) the automatic creation of logical assertions that capture the way one or more source schemas are mapped into a target schema, and 2) the generation of transformation queries or programs that transform a source data instance into a target data instance.
40383321
Schema Mapping as Query Discovery
1321953
Data-driven understanding and refinement of schema mappings
At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distinguish effectively between alternative schema mappings. These operators permit a user to quickly and intuitively build and refine complex data transformation queries that map one data source into another.
12331697
The Clio project: managing heterogeneity
Clio is a system for managing and facilitating the complex tasks of heterogeneous data transformation and integration. In Clio, we have collected together a powerful set of data management techniques that have proven invaluable in tackling these difficult problems. In this paper, we present the underlying themes of our approach and present a brief case study.
18318781
Schema Discovery
Structured data is distinguished from unstructured data by the presence of a schema describing the logical structure and semantics of the data. The schema is th e means through which we understand and query the underlying data. The schema permits the more sophi ticated structured queries that are not possible over schema-less data. Most systems assume that th e schema is predefined and is an accurate reflection of the data. This assumption is often not valid in n etworked databases that may contain data originating from many sources and may not be valid within leg acy databases where the semantics of data have evolved over time. As a result, querying and tasks that d epend on structured queries (including data integration and schema mapping) may not be effective. I n this paper, we consider the problem of discovering schemas from data. We focus on discovering prop erties of data that can be exploited in querying and transforming data. Finally, we very briefly con sider the suitability of mining approaches to the task of schema discovery.
7371269
Translating Web Data
We present a novel framework for mapping between any combination of XML and relational schemas, in which a high-level, user-specified mapping is translated into semantically meaningful queries that transform source data into the target representation. Our approach works in two phases. In the first phase, the high-level mapping, expressed as a set of inter-schema correspondences, is converted into a set of mappings that capture the design choices made in the source and target schemas (including their hierarchical organization as well as their nested referential constraints). The second phase translates these mappings into queries over the source schemas that produce data satisfying the constraints and structure of the target schema, and preserving the semantic relationships of the source. Nonnull target values may need to be invented in this process. The mapping algorithm is complete in that it produces all mappings that are consistent with the schema constraints. We have implemented the translation algorithm in Clio, a schema mapping tool, and present our experience using Clio on several real schemas.
1267904
Detecting Acromegaly: Screening for Disease with a Morphable Model
Acromegaly is a rare disorder which affects about 50 of every million people. The disease typically causes swelling of the hands, feet, and face, and eventually permanent changes to areas such as the jaw, brow ridge, and cheek bones. The disease is often missed by physicians and progresses beyond where it might if it were identified and treated earlier. We consider a semi-automated approach to detecting acromegaly, using a novel combination of support vector machines (SVMs) and a morphable model. Our training set consists of 24 frontal photographs of acromegalic patients and 25 of disease-free subjects. We modelled each subject's face in an analysis-by-synthesis loop using the three-dimensional morphable face model of Blanz and Vetter. The model parameters capture many features of the 3D shape of the subject's head from just a single photograph, and are used directly for classification. We report encouraging results of a classifier built from the training set of real human subjects.
5333493
A Heuristic Approach to the Multiple Diagnoses Problem
A new heuristic model for the diagnosis of multiple related disorders in a patient case is presented. The model constructs differential diagnoses of single and related disorders, based on a medical knowledge base containing disease descriptions and linkages among diseases in the knowledge base.
15320762
Narratives in the Situation Calculus
A narrative is a course of real events about which we might have incomplete information. Formalisms for reasoning about action may be broadly divided into those which are narrative-based, such as the Event Calculus of Kowalski and Sergot, and those which reason on the level of hypothetical sequences of actions, in particular the Situation Calculus. This paper bridges the gap between these types of formalism by supplying a technique for linking incomplete narrative descriptions to Situation Calculus domain formulae written in the usual style using a Result function. Particular attention is given to actions with duration and overlapping actions. By illuminating the relationship between these two diierent styles of representation, the paper moves us one step closer to a full understanding of the space of all possible formalisms for reasoning about action.