Constructing a Focused Taxonomy from a Document Collection

  • Olena Medelyan
  • Steve Manion
  • Jeen Broekstra
  • Anna Divoli
  • Anna-Lan Huang
  • Ian H. Witten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

We describe a new method for constructing custom taxonomies from document collections. It involves identifying relevant concepts and entities in text; linking them to knowledge sources like Wikipedia, DBpedia, Freebase, and any supplied taxonomies from related domains; disambiguating conflicting concept mappings; and selecting semantic relations that best group them hierarchically. An RDF model supports interoperability of these steps, and also provides a flexible way of including existing NLP tools and further knowledge sources. From 2000 news articles we construct a custom taxonomy with 10,000 concepts and 12,700 relations, similar in structure to manually created counterparts. Evaluation by 15 human judges shows the precision to be 89% and 90% for concepts and relations respectively; recall was 75% with respect to a manually generated taxonomy for the same domain.

Keywords

Concept Mapping Document Collection News Article Knowledge Source Related Taxonomy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Olena Medelyan
    • 1
  • Steve Manion
    • 1
  • Jeen Broekstra
    • 1
  • Anna Divoli
    • 1
  • Anna-Lan Huang
    • 2
  • Ian H. Witten
    • 2
  1. 1.Pingar ResearchAucklandNew Zealand
  2. 2.University of WaikatoHamiltonNew Zealand

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