Automatically Harvesting and Ontologizing Semantic Relations

  • Authors:

  • Patrick Pantel;Marco Pennacchiotti

  • Affiliations:

  • Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA 90292 pantel@isi.edu;Dept. of Computational Linguistics, Saarland University, Germany pennacchiotti@coli.uni-sb.de

  • Venue:
  • Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
  • Year:
  • 2008

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Abstract

With the advent of the Web and the explosion of available textual data, it is key for modern natural language processing systems to access, represent and reason over large amounts of knowledge in semantic repositories. Separately, the knowledge representation and natural language processing communities have been developing representations/engines for reasoning over knowledge and algorithms for automatically harvesting knowledge from textual data, respectively. There is a pressing need for collaboration between the two communities to provide large-scale robust reasoning capabilities for knowledge rich applications like question answering. In this chapter, we propose one small step by presenting algorithms for harvesting semantic relations from text and then automatically linking the knowledge into existing semantic repositories. Experimental results show better than state of the art performance on both relation harvesting and ontologizing tasks.