The document discusses using semantic web technologies in enterprise architecture to address goals like process optimization and data quality. It introduces key concepts like ontologies, RDF, and OWL, and how they can be used to formally represent information and allow machines to extract meaning. This can help with modeling languages, data specification, and deriving other artifacts like databases and event models. Tools are available to work with these technologies and help achieve goals like semantically unambiguous specifications that align data across systems. Future trends may include semantic databases and other applications that leverage the semantic web.

1. The Semantic Web for Enterprise Architecture James Lapalme

10. Semantic Web

14. Evoluation

15. Just Little Theory

16. Set theory

17. Function/Relation

18. Ressource Description Framework URI URI URI

22. Example

23. Semantic Web Stack

24. Information Modeling

27. Glossary vs Taxonomy

29. Modeling Technologies (key differences) Generalization by Restriction Disjoint Sub-Classing Multiple Inheritance XSD Schema Conceptual ER UML

30. Applications to EA

38. Questions