Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.

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What are ontologies, and why do we need them?

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

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Web mining research: a survey

Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.

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Survey of graph database models

1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

As researchers continue to create new languages in the hope of developing a Semantic Web, they still lack consensus on a standard. The authors describe how Protege-2000, a tool for ontology development and knowledge acquisition, can be adapted for editing models in different Semantic Web languages.

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Creating Semantic Web contents with Protege-2000

Metadata for the World Wide Web is important, but metadata for Peer-to-Peer (P2P) networks is absolutely crucial. In this paper we discuss the open source project Edutella which builds upon metadata standards defined for the WWW and aims to provide an RDF-based metadata infrastructure for P2P applications, building on the recently announced JXTA Framework. We describe the goals and main services this infrastructure will provide and the architecture to connect Edutella Peers based on exchange of RDF metadata. As the query service is one of the core services of Edutella, upon which other services are built, we specify in detail the Edutella Common Data Model (ECDM) as basis for the Edutella query exchange language (RDF-QEL-i) and format implementing distributed queries over the Edutella network. Finally, we shortly discuss registration and mediation services, and introduce the prototype and application scenario for our current Edutella aware peers.

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EDUTELLA: a P2P networking infrastructure based on RDF

This paper presents TRIPLE, a layered and modular rule language for the Semantic Web [1]. TRIPLE is based on Horn logic and borrows many basic features from F-Logic [11] but is especially designed for querying and transforming RDF models [20].TRIPLE can be viewed as a successor of SiLRI (Simple Logic-based RDF Interpreter [5]). One of the most important differences to F-Logic and SiLRI is that TRIPLE does not have a fixed semantics for object-oriented features like classes and inheritance. Its layered architecture allows such features to be easily defined for different object-oriented and other data models like UML, Topic Maps, or RDF Schema [19]. Description logics extensions of RDF (Schema) like OIL [17] and DAML+OIL [3] that cannot be fully handled by Horn logic are provided as modules that interact with a description logic classifier, e.g. FaCT [9], resulting in a hybrid rule language. This paper sketches syntax and semantics of TRIPLE.

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TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web

To meet the growing need for enterprise-wide knowledge management, the authors have developed and fielded a three-layered model for processing knowledge. This article shows how their organizational memory serves as an intelligent assistant and deals with both formal and non-formal knowledge elements in a task-oriented fashion.

Toward a technology for organizational memories

In this paper we describe a plug-in (OntoLT) for the widely used Protege ontology development tool that supports the interactive extraction and/or extension of ontologies from text. The OntoLT approach provides an environment for the integration of linguistic analysis in ontology engineering through the definition of mapping rules that map linguistic entities in annotated text collections to concept and attribute candidates (i.e. Protege classes and slots). The paper ex-plains this approach in more detail and discusses some initial experiments on deriving a shallow ontology for the neurology domain from a corresponding collection of neurological scientific abstracts.

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Michael Sintek

Papers

Creating Semantic Web contents with Protege-2000

EDUTELLA: a P2P networking infrastructure based on RDF

TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web

Toward a technology for organizational memories

A protégé plug-in for ontology extraction from text based on linguistic analysis