A Self-Training Approach for Resolving Object Coreference on the Semantic Web (2011)
| Citations: | 6 - 0 self |
BibTeX
@MISC{Hu11aself-training,
author = {Wei Hu and Jianfeng Chen and Yuzhong Qu},
title = {A Self-Training Approach for Resolving Object Coreference on the Semantic Web},
year = {2011}
}
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Abstract
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
