Disparate information, spread over various sources, in various formats, and with inconsistent semantics is a major obstacle for enterprises to use this information at its full potential. Information Grids should allow for the effective access, extraction and linking of dispersed information. Currently Europe's coporations spend over 10 Billion EUR to deal with these problems. This book will demonstrate the applicability of grid technologies to industry. To this end, it gives a detailed insight on how ontology technology can be used to manage dispersed information assets more efficiently.…mehr
Disparate information, spread over various sources, in various formats, and with inconsistent semantics is a major obstacle for enterprises to use this information at its full potential. Information Grids should allow for the effective access, extraction and linking of dispersed information. Currently Europe's coporations spend over 10 Billion EUR to deal with these problems.
This book will demonstrate the applicability of grid technologies to industry. To this end, it gives a detailed insight on how ontology technology can be used to manage dispersed information assets more efficiently. The book is based on experiences from the COG (Corporate Ontology Grid) project, carried out jointly by three leading industrial players and the Digital Enterprise Research Institute Austria. Through comparisons of this project with alternative technologies and projects, it provides hands-on experience and best practice examples to act as a reference guide for their development.
Information Integration with Ontologies: Ontology based Information Integration in an Industrial Setting is ideal for technical experts and computer researchers in the IT-area looking to achieve integration of heterogeneous information and apply ontology technologies and techniques in practice. It will also be of great benefit to technical decision makers seeking information about ontology technologies and the scientific audience, interested in achievements towards the application of ontologies in an industrial setting.
The book is authored by members of the Digital Enterprise Research Institute (DERI) headed by Professor Dieter Fensel. Professor Fensel is the scientific director of DERI at the National University of Ireland, Galway, based on a large grant acquired from Science Foundation Ireland (SFI). His current research interests include Ontologies, semantic web, web services, knowledge management, enterprise application integration, and electronic commerce. He is a major scientific player in the area of the semantic web and has authored and co-edited 9 books, and more than 150 publications in journals and conferences. He is associate editor of the Knowledge and Information Systems: An International Journal (KAIS), IEEE Intelligent Systems, the Electronic Transactions on Artificial Intelligence (ETAI), Web Intelligence and Agent Systems (WIAS), Elsevier's Journal on Web Semantics: Science, Services and Agents on the World Wide Web and the Lecture Notes in Computer Science (LNCS) subline entitled "Semantics in Data Management".
Inhaltsangabe
Foreword. Acknowledgements. List of Figures. 1 Introduction. 1.1 Finding a Way Out of the Dilemma. 1.2 The Background to this Book. 1.3 The Structure of the Book. 1.3.1 Data modelling and ontologies. 1.3.2 Information integrationwith relational databases and XML. 1.3.3 The show case. 1.3.4 Semantic information integration. 1.3.5 Data source queries. 1.3.6 Generating transformations. 1.3.7 Best Practices and Methodologies. 2 Data Modelling and Ontologies. 2.1 The Information Integration Problem. 2.1.1 How databases view the world. 2.1.2 How ontologies view the world. 2.1.3 Comparison. 2.2 Semantic Information Management. 2.2.1 Principles. 2.2.2 The methodology. 2.3 Conclusions. 3 Information Integration with Relational Databases and XML. 3.1 Introduction. 3.1.1 Areas of data integration. 3.1.2 Business drivers of data integration. 3.1.3 Scope of this chapter. 3.2 Relational Database Integration. 3.2.1 Integration considerations. 3.2.2 Integration approaches/degrees. 3.2.3 Data centralization, sharing and federation. 3.2.4 Integration characteristics. 3.3 XML-based Integration. 3.3.1 XML tools. 3.3.2 XML and objects. 3.3.3 XML and databases. 3.3.4 XML transformations. 3.3.5 XML, eCommerce and Web services. 3.4 Conclusions. 3.4.1 Summary. 3.4.2 Variety in data integration. 4 The Show Case. 4.1 Data Sources. 4.2 Identifying Overlaps between the Data Sources. 4.3 Current Ways of Dealing with Heterogeneity. 5 Semantic Information Integration. 5.1 Approaches in Information Integration. 5.2 Mapping Heterogeneous Data Sources. 5.2.1 The Unicorn Workbench. 5.2.2 Ontology construction and rationalization in the COG project. 5.3 Other Methods and Tools. 5.3.1 The MOMIS approach. 5.3.2 InfoSleuth. 5.3.3 OBSERVER. 5.3.4 Ontology mapping in the KRAFT project. 5.3.5 PROMPT. 5.3.6 Chimæra. 5.3.7 ONION. 5.3.8 Other ontology merging methods. 5.4 Comparison of the Methods. 5.4.1 Comparison criteria. 5.4.2 Comparing the methodologies for semantic schema integration. 5.5 Conclusions and Future Work. 5.5.1 Limitations of the Unicorn Workbench and future work. 6 Data Source Queries. 6.1 Querying Disparate Data Sources Using the Unicorn Workbench. 6.1.1 Queries in the Unicorn Workbench. 6.1.2 Transforming conceptual queries into database queries. 6.1.3 Limitations of the current approach. 6.2 Querying Disparate Data Sources. 6.2.1 The querying architecture in the COG project. 6.2.2 Querying in the COG showcase. 6.2.3 Overcoming the limitations of the Unicorn Workbench. 6.3 Related Work. 6.3.1 Ontology query languages. 6.4 Conclusions. 7 Generating Transformations. 7.1 Information Transformation in the COG Project. 7.1.1 Generating transformations with the Unicorn Workbench. 7.1.2 Automatic generation of transformations in the COG project. 7.2 Other Information Transformation Approaches. 7.2.1 Approaches that perform instance transformation. 7.2.2 Approaches that do not perform instance transformation. 7.3 Conclusions, Limitations and Extensions. 8 Best Practices and Methodologies Employed. 8.1 Best Practices. 8.1.1 Selective mapping. 8.1.2 Domain vs application modelling. 8.1.3 Global-as-view vs local-as-view. 8.2 Lessons Learned. 8.2.1 Quality of global model depends on local models. 8.2.2 Refinement of ontological concepts. 8.2.3 Automation is hard to achieve in real-life situations. 8.2.4 Queries vs transformations. 8.3 Conclusions. 9 Conclusion. References. Glossary. Index.
Foreword. Acknowledgements. List of Figures. 1 Introduction. 1.1 Finding a Way Out of the Dilemma. 1.2 The Background to this Book. 1.3 The Structure of the Book. 1.3.1 Data modelling and ontologies. 1.3.2 Information integrationwith relational databases and XML. 1.3.3 The show case. 1.3.4 Semantic information integration. 1.3.5 Data source queries. 1.3.6 Generating transformations. 1.3.7 Best Practices and Methodologies. 2 Data Modelling and Ontologies. 2.1 The Information Integration Problem. 2.1.1 How databases view the world. 2.1.2 How ontologies view the world. 2.1.3 Comparison. 2.2 Semantic Information Management. 2.2.1 Principles. 2.2.2 The methodology. 2.3 Conclusions. 3 Information Integration with Relational Databases and XML. 3.1 Introduction. 3.1.1 Areas of data integration. 3.1.2 Business drivers of data integration. 3.1.3 Scope of this chapter. 3.2 Relational Database Integration. 3.2.1 Integration considerations. 3.2.2 Integration approaches/degrees. 3.2.3 Data centralization, sharing and federation. 3.2.4 Integration characteristics. 3.3 XML-based Integration. 3.3.1 XML tools. 3.3.2 XML and objects. 3.3.3 XML and databases. 3.3.4 XML transformations. 3.3.5 XML, eCommerce and Web services. 3.4 Conclusions. 3.4.1 Summary. 3.4.2 Variety in data integration. 4 The Show Case. 4.1 Data Sources. 4.2 Identifying Overlaps between the Data Sources. 4.3 Current Ways of Dealing with Heterogeneity. 5 Semantic Information Integration. 5.1 Approaches in Information Integration. 5.2 Mapping Heterogeneous Data Sources. 5.2.1 The Unicorn Workbench. 5.2.2 Ontology construction and rationalization in the COG project. 5.3 Other Methods and Tools. 5.3.1 The MOMIS approach. 5.3.2 InfoSleuth. 5.3.3 OBSERVER. 5.3.4 Ontology mapping in the KRAFT project. 5.3.5 PROMPT. 5.3.6 Chimæra. 5.3.7 ONION. 5.3.8 Other ontology merging methods. 5.4 Comparison of the Methods. 5.4.1 Comparison criteria. 5.4.2 Comparing the methodologies for semantic schema integration. 5.5 Conclusions and Future Work. 5.5.1 Limitations of the Unicorn Workbench and future work. 6 Data Source Queries. 6.1 Querying Disparate Data Sources Using the Unicorn Workbench. 6.1.1 Queries in the Unicorn Workbench. 6.1.2 Transforming conceptual queries into database queries. 6.1.3 Limitations of the current approach. 6.2 Querying Disparate Data Sources. 6.2.1 The querying architecture in the COG project. 6.2.2 Querying in the COG showcase. 6.2.3 Overcoming the limitations of the Unicorn Workbench. 6.3 Related Work. 6.3.1 Ontology query languages. 6.4 Conclusions. 7 Generating Transformations. 7.1 Information Transformation in the COG Project. 7.1.1 Generating transformations with the Unicorn Workbench. 7.1.2 Automatic generation of transformations in the COG project. 7.2 Other Information Transformation Approaches. 7.2.1 Approaches that perform instance transformation. 7.2.2 Approaches that do not perform instance transformation. 7.3 Conclusions, Limitations and Extensions. 8 Best Practices and Methodologies Employed. 8.1 Best Practices. 8.1.1 Selective mapping. 8.1.2 Domain vs application modelling. 8.1.3 Global-as-view vs local-as-view. 8.2 Lessons Learned. 8.2.1 Quality of global model depends on local models. 8.2.2 Refinement of ontological concepts. 8.2.3 Automation is hard to achieve in real-life situations. 8.2.4 Queries vs transformations. 8.3 Conclusions. 9 Conclusion. References. Glossary. Index.
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