document: 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 semantically coreferent URIs based on owl : sameAs , ( inverse ) functional properties and ( max - ) cardinalities , and then extend such kernel iteratively in terms of discriminative property-value pairs in the descriptions of URIs . In particular , the discriminability is learned with a statistical measurement , which not only exploits key characteristics for representing an object , but also takes into account the matchability between properties from pragmatics . In addition , frequent property combinations are mined to improve the accuracy of the resolution . We implement a scalable system and demonstrate that our approach achieves good precision and recall for resolving object coreference , on both benchmark and large-scale datasets .
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