About: Hundreds of millions of tables on the World-Wide Web contain a considerable wealth of high-quality relational data, which has already been viewed as an important kind of sources for knowledge extraction. In order to extract the semantics of web tables to produce machine-readable knowledge, one of the critical steps is table entity linking, which maps the mentions in table cells to their referent entities in knowledge bases. In this paper, we propose a novel model JHSTabEL, which converts table entity linking into a sequence decision problem and uses hybrid semantic features to disambiguate the mentions in web tables. This model captures local semantics of the mentions and entities from different semantic aspects, and then makes full use of the information of previously referred entities for the subsequent entity disambiguation. The decisions are made from a global perspective to jointly disambiguate the mentions in the same column. Experimental results show that our proposed model significantly outperforms the state-of-the-art methods.   Goto Sponge  NotDistinct  Permalink

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  • Hundreds of millions of tables on the World-Wide Web contain a considerable wealth of high-quality relational data, which has already been viewed as an important kind of sources for knowledge extraction. In order to extract the semantics of web tables to produce machine-readable knowledge, one of the critical steps is table entity linking, which maps the mentions in table cells to their referent entities in knowledge bases. In this paper, we propose a novel model JHSTabEL, which converts table entity linking into a sequence decision problem and uses hybrid semantic features to disambiguate the mentions in web tables. This model captures local semantics of the mentions and entities from different semantic aspects, and then makes full use of the information of previously referred entities for the subsequent entity disambiguation. The decisions are made from a global perspective to jointly disambiguate the mentions in the same column. Experimental results show that our proposed model significantly outperforms the state-of-the-art methods.
subject
  • Evidence-based medicine
  • Concepts in logic
  • Grammar
  • Patent law
  • Social philosophy
  • Semantics
  • Meaning (philosophy of language)
  • Web technology
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