Attribute extraction and scoring: A probabilistic approach

Taesung Lee, Zhongyuan Wang, Haixun Wang, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

Knowledge bases, which consist of concepts, entities, attributes and relations, are increasingly important in a wide range of applications. We argue that knowledge about attributes (of concepts or entities) plays a critical role in inferencing. In this paper, we propose methods to derive attributes for millions of concepts and we quantify the typicality of the attributes with regard to their corresponding concepts. We employ multiple data sources such as web documents, search logs, and existing knowledge bases, and we derive typicality scores for attributes by aggregating different distributions derived from different sources using different methods. To the best of our knowledge, ours is the first approach to integrate concept- and instance-based patterns into probabilistic typicality scores that scale to broad concept space. We have conducted extensive experiments to show the effectiveness of our approach.

Original languageEnglish
Title of host publicationICDE 2013 - 29th International Conference on Data Engineering
Pages194-205
Number of pages12
DOIs
Publication statusPublished - 2013
Event29th International Conference on Data Engineering, ICDE 2013 - Brisbane, QLD, Australia
Duration: 2013 Apr 82013 Apr 11

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other29th International Conference on Data Engineering, ICDE 2013
Country/TerritoryAustralia
CityBrisbane, QLD
Period13/4/813/4/11

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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