A New Method for Coreference Resolution in the Web of Linked Data Based On Machine Learning

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LearningLeila Namnik
Mehran Mohsenzadeh, Mashallah Abbasi Dezfouli


Web of Linked Data forms a single, globally distributed data space. Establishing RDF links between overlapping but separately constituted RDF datasets still represents one of the most important challenges to achieve the vision of the Web of Linked Data. In Linked Data environment, an object is likely to be denoted with multiple URIs by different data providers. Object coreference resolution is to identify “equivalent†URIs that denotes the same object. One of the most important types of RDF links are “Identity Linksâ€, which point at coreferent objects. By common agreement, Web of Linked Data uses owl:SameAs predicate to state identity links. Driven by the Linking Open Data (LOD) project, millions of URIs have been explicitly linked via owl:sameAs, but potentially coreferent ones are still considerable. Coreference resolution and data linking often relies on fuzzy similarity functions comparing relevant characteristics of objects in the considered datasets and manually tuned metrics for estimating similarity between objects. In this paper, we describe an approach for object coreference resolution in Linked Data, which relies on supervised learning and support vector machines. We propose to employ different similarity functions and combine them with a learning scheme. Initial experiments applying this approach to public datasets have produced encouraging results.


Keywords: Coreference Resolution, Data Interlinking, Linked Data, Semantic Web, SVM.


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