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KR techniques can improve precision and recall of IE, and IE techniques can help solve the knowledge acquisition bottleneck. But combining these techniques presents serious challenges to both; KR techniques do not scale and are extremely brittle in the presence of errors, whereas IE techniques are extremely error prone and even "correct" extraction often has imprecise semantics. In this talk, I will discuss the issues that arise in this combination and how we are addressing them.
Chris Welty is a Research Scientist at the IBM T.J. Watson Research Center in New York. Previously, he taught Computer Science at Vassar College, taught at and received his Ph.D. from Rensselaer Polytechnice Institute, and accumulated over 14 years of teaching experience before moving to industrial research. Chris' principal area of research is Knowledge Representation, specifically ontologies and the semantic web, and he spends most of his time applying this technology to Information Retrieval and, in the past, Software Engineering. Dr. Welty serves on the steering committee of the Formal Ontology in Information Systems Conferences, as chair of the Knowledge Representation Conference, on the advisory board of IJCAI, on the editorial boards of AI Magazine, The Journal of Applied Ontology, and The Journal of Web Semantics, was an editor in the W3C Web Ontology Working Group. Chris Welty's work on ontologies and ontology methodology has appeared in CACM, and numerous other publications, and he now coordinates the Ontology Engineering and Patterns task force of the W3C.