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A self-training approach for resolving object coreference on the semantic web

8 years 6 months ago
A self-training approach for resolving object coreference on the semantic web
An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify “equivalent” URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs statements, but potentially coreferent ones are still considerable. Existing approaches address the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which finds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between property-value pairs, which is not always accurate enough. In this paper, we propose a self-training approach for object coreference resolution on the Semantic Web, which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates. For an object URI, we firstly establish a kernel that consists of sema...
Wei Hu, Jianfeng Chen, Yuzhong Qu
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where WWW
Authors Wei Hu, Jianfeng Chen, Yuzhong Qu
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