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Abstract
Recently, many corpus-based and web-based knowledge acquisition systems have been proposed for creating lexical resources. Not many attempts, however, have been made at ontologizing these resources. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexico-syntactic patterns over the Web. We provide the resource, called VerbOcean, for download at http://semantics.isi.edu/ocean/. We will discuss current work on ontologizing lexical resources like VerbOcean. Using an automatic algorithm, we assign a grammatical template to each node of an ontology. The challenge lies in disambiguating these templates. Benefits of this work potentially include the disambiguation of VerbOcean, the disambiguation of new conceptualizations, improved unsupervised word sense disambiguation, and the personalization of ontologies, like WordNet, to a particular domain.

Biography
Dr. Patrick Pantel is currently a Research Scientist in the Natural Language Group at the USC Information Sciences Institute where he does research in semi-automatic ontology construction, text mining, knowledge acquisition, and machine learning. In 2003, he received a Ph.D. in Computing Science from the University of Alberta in Edmonton, Canada. He is the recipient of various prestigious awards, including the University of Manitoba gold medal for the Faculty of Science, two national scholarships from the Natural Sciences and Engineering Research Council of Canada and the Izaak Walton Killam Memorial scholarship.