29 November 2017: Frank van Harmelen The Empirical turn in Knowledge Representation Frank van Harmelen Vrije Universiteit Amsterdam Traditionally, Knowledge Representation (as a subfield of AI) has had a rather prescriptive attitude. Each new knowledge representation formalism was implicitly or explicitly a prescription for the correct (or least an effective) way of formalising knowledge in a computer processable form. Recent years have seen the emergence of very (VERY) large knowledge bases on the Web. Using language like RDF and OWL, it is now commonplace to publish, combine and query knowledge bases of tens of millions of facts and rules. These very (VERY) large knowledge bases allow us to instead take an empirical attitude: how do people and machines actually represent knowledge at this scale? Can we extract useful patterns? Do these patterns tell us something about what our machines should do? Or what our knowledge representation languages should have looked like? Bio: Frank van Harmelen got his PhD from Edinburgh in 1989 under supervision of Alan Bundy. He has been involved in Semantic Web research since the early days, has been one of the designers of the widely used OWL Web Ontology Language, and received the Semantic Web 10-year impact award for his work on Sesame, one of the earliest engines for storying and querying knowledge-bases on the web. He is member of the Dutch Royal Academy of Science and of the European Academy of Science. Nov 29 2017 15.00 - 16.00 29 November 2017: Frank van Harmelen The Empirical turn in Knowledge Representation Appleton Tower, 2.14
29 November 2017: Frank van Harmelen The Empirical turn in Knowledge Representation Frank van Harmelen Vrije Universiteit Amsterdam Traditionally, Knowledge Representation (as a subfield of AI) has had a rather prescriptive attitude. Each new knowledge representation formalism was implicitly or explicitly a prescription for the correct (or least an effective) way of formalising knowledge in a computer processable form. Recent years have seen the emergence of very (VERY) large knowledge bases on the Web. Using language like RDF and OWL, it is now commonplace to publish, combine and query knowledge bases of tens of millions of facts and rules. These very (VERY) large knowledge bases allow us to instead take an empirical attitude: how do people and machines actually represent knowledge at this scale? Can we extract useful patterns? Do these patterns tell us something about what our machines should do? Or what our knowledge representation languages should have looked like? Bio: Frank van Harmelen got his PhD from Edinburgh in 1989 under supervision of Alan Bundy. He has been involved in Semantic Web research since the early days, has been one of the designers of the widely used OWL Web Ontology Language, and received the Semantic Web 10-year impact award for his work on Sesame, one of the earliest engines for storying and querying knowledge-bases on the web. He is member of the Dutch Royal Academy of Science and of the European Academy of Science. Nov 29 2017 15.00 - 16.00 29 November 2017: Frank van Harmelen The Empirical turn in Knowledge Representation Appleton Tower, 2.14
Nov 29 2017 15.00 - 16.00 29 November 2017: Frank van Harmelen The Empirical turn in Knowledge Representation
