Iterative algorithm for inferring entity types from enumerative descriptions

Qian Chen, Mizuho Iwaihara

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


Entity type matching has many real world applications, especially in entity clustering, de-duplication and efficient query processing. Current methods to extract entities from text usually disregard regularities in the order of entities appearing in the text. In this paper, we focus on enumerative descriptions which enlist entity names in a certain hidden order, often occurring in web documents as listings and tables. We propose an algorithm to discover entity types from enumerative descriptions, where a type hierarchy is known but enumerating orders are hidden and heterogeneous, and partial entity-type mappings are given as seed instances. Our algorithm is iterative: We extract skeletons from syntactic patterns, then train a hidden Markov model to find an optimum enumerating order from seed instances and skeletons, to find a best-fit entity-type assignment.

Original languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450327459
Publication statusPublished - 2014 Apr 7
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 2014 Apr 72014 Apr 11


Other23rd International Conference on World Wide Web, WWW 2014
Country/TerritoryKorea, Republic of


  • Hidden markov model
  • Information extraction
  • RDF graph

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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