This valuable resource provides a detailed insight into how ontology technology can be used to achieve optimum performance levels in the management of disparate information. Through practical experience this real–world approach focuses on the realization of information grids, based on ontologies, and their application in an industrial setting.
- Presents theory–based practical advice on integrating heterogeneous data sources using ontology technologies
- Includes the best practice examples and methodologies employed gleaned from the implementation of COG (Corporate Ontology Grid)
- Compares and contrasts data models and ontologies, illustrating these with real–world applications
- Includes a comprehensive section on ontology querying and generating transformations via an ontological grid
Information Integration with Ontologies will be of interest to IT technicians needing advice and examples on the successful integration of heterogeneous information and the application of ontology technology. It will also appeal to technical decision makers involved in real–world applications.
List of Figures.
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.
2.2 Semantic Information Management.
2.2.2 The methodology.
3 Information Integration with Relational Databases and XML.
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.
3.4.2 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.
5.3.4 Ontology mapping in the KRAFT project.
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.
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.