Ontology-driven automatic entity disambiguation in unstructured text

  • Authors:

  • Joseph Hassell;Boanerges Aleman-Meza;I. Budak Arpinar

  • Affiliations:

  • Large Scale Distributed Information Systems (LSDIS) Lab Computer Science Department, University of Georgia, Athens, GA;Large Scale Distributed Information Systems (LSDIS) Lab Computer Science Department, University of Georgia, Athens, GA;Large Scale Distributed Information Systems (LSDIS) Lab Computer Science Department, University of Georgia, Athens, GA

  • Venue:
  • ISWC'06 Proceedings of the 5th international conference on The Semantic Web
  • Year:
  • 2006

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Abstract

Precisely identifying entities in web documents is essential for document indexing, web search and data integration. Entity disambiguation is the challenge of determining the correct entity out of various candidate entities. Our novel method utilizes background knowledge in the form of a populated ontology. Additionally, it does not rely on the existence of any structure in a document or the appearance of data items that can provide strong evidence, such as email addresses, for disambiguating person names. Originality of our method is demonstrated in the way it uses different relationships in a document as well as from the ontology to provide clues in determining the correct entity. We demonstrate the applicability of our method by disambiguating names of researchers appearing in a collection of DBWorld posts using a large scale, real-world ontology extracted from the DBLP bibliography website. The precision and recall measurements provide encouraging results.