Graph-based wrong IsA relation detection in a large-scale lexical taxonomy

Jiaqing Liang, Yanghua Xiao, Yi Zhang, Seung Won Hwang, Haixun Wang

Research output: Contribution to conferencePaper

8 Citations (Scopus)

Abstract

Knowledge base(KB) plays an important role in artificial intelligence. Much effort has been taken to both manually and automatically construct web-scale knowledge bases. Comparing with manually constructed KBs, automatically constructed KB is broader but with more noises. In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships. We find that these taxonomies usually contain cycles, which are often introduced by incorrect isA relations. Inspired by this observation, we introduce two kinds of models to detect incorrect isA relations from cycles. The first one eliminates cycles by extracting directed acyclic graphs, and the other one eliminates cycles by grouping nodes into different levels. We implement our models on Probase, a state-of-the-art, automatically constructed, web-scale taxonomy. After processing tens of millions of relations, our models eliminate 74 thousand wrong relations with 91% accuracy.

Original languageEnglish
Pages1178-1184
Number of pages7
Publication statusPublished - 2017 Jan 1
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 2017 Feb 42017 Feb 10

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period17/2/417/2/10

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Liang, J., Xiao, Y., Zhang, Y., Hwang, S. W., & Wang, H. (2017). Graph-based wrong IsA relation detection in a large-scale lexical taxonomy. 1178-1184. Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States.