Watson Goes Back to School - And what it tells us about the evolving role of semantic technology
Share this Session:
  Christopher Welty   Christopher Welty
Research Scientist
IBM Research


Monday, October 15, 2012
04:45 PM - 05:30 PM
Level:  Case Study

Location:  Grand Ballroom

In the traditional vision of AI, understanding flowed from perception through language to knowledge. It had always been envisioned that this understanding would be in some precise and unambiguous knowledge representation, and that all meaning processing would happen in this representation. This is the root of all semantic technology today.

However, over time, the failure of the AI community to achieve this end-to-end vision made many, especially those in NLP, question the endpoint. In other words, to doubt the value of semantic technology. In this talk, we show that it was the vision, not the technology, that deserved to be doubted. Semantic technology has significant value in accomplishing tasks that require understanding, but it is not the endpoint.

Chris Welty is a research scientist at the IBM T.J. Watson Research Center in New York. Before coming to IBM, he taught computer science at Vassar College and at Rensselaer Polytechnic Institute, where he received a Ph.D. in 1995. Before moving to industrial research, he accumulated more than 14 years of teaching experience. Dr. Welty has worked to advance semantic technology standards, and the use of semantic technology and linked open data in natural language processing. The most notable examples of this are in the Watson Question-Answering system itself, which uses RDF and Linked Data extensively. He has also served this community as co-chair of the W3C Rules Interchange Format Working Group (RIF), co-editor of the original OWL Guide, co-program chair of ISWC-2011, and the AI&Web track of AAAI-12. He is the current Human Language Technologies Area Editor for The Journal of Web Semantics. Before the success of the Watson project, Dr. Welty was best known for his work on Ontologies.

Close Window