About: Joint Entity Linking for Web Tables with Hybrid Semantic Matching

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Joint Entity Linking for Web Tables with Hybrid Semantic Matching
Creator	Xie, Jie Li, Zhenzhen Cao, Cong Guan, Yangyang Liu, Yanbing Lu, Yuhai
Source	PMC
abstract	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.
has issue date	2020-06-15(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50417-5_46
has license	no-cc
sha1sum (hex)	eb94c6ebc6de0e35324b7812eb4ae54c7a87b0a4
schema:url	https://doi.org/10.1007/978-3-030-50417-5_46
resource representing a document's title	Joint Entity Linking for Web Tables with Hybrid Semantic Matching
has PubMed Central identifier	PMC7302827
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:eb94c6ebc6de0e35324b7812eb4ae54c7a87b0a4#body_text
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