The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Ontology-driven automatic entity disambiguation in unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Mining information for instance unification
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Automatically generating data linkages using a domain-independent candidate selection approach
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Domain-Independent Entity Coreference for Linking Ontology Instances
Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
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In this paper, we present a novel entity coreference algorithm for Semantic Web instances. The key issues include how to locate context information and how to utilize the context appropriately. To collect context information, we select a neighborhood (consisting of triples) of each instance from the RDF graph. To determine the similarity between two instances, our algorithm computes the similarity between comparable property values in the neighborhood graphs. The similarity of distinct URIs and blank nodes is computed by comparing their outgoing links. To provide the best possible domain-independent matches, we examine an appropriate way to compute the discriminability of triples. To reduce the impact of distant nodes, we explore a distance-based discounting approach. We evaluated our algorithm using different instance categories in two datasets. Our experiments show that the best results are achieved by including both our triple discrimination and discounting approaches.