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Author Song, Dezhao ♦ Heflin, Jeff
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Entity coreference ♦ Discriminability ♦ Domain-independence ♦ Ontology ♦ Semantic web
Abstract The objective of entity coreference is to determine if different mentions (e.g., person names, place names, database records, ontology instances, etc.) refer to the same real word object. Entity coreference algorithms can be used to detect duplicate database records and to determine if two Semantic Web instances represent the same underlying real word entity. The key issues in developing an entity coreference algorithm include how to locate context information and how to utilize the context appropriately. In this article, we present a novel entity coreference algorithm for ontology instances. For scalability reasons, we select a neighborhood of each instance from an 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. In an attempt to reduce the impact of distant nodes on the final similarity measure, we explore a distance-based discounting approach. To provide the best possible domain-independent matches, we propose an approach to compute the discriminability of triples in order to assign weights to the context information. We evaluated our algorithm using different instance categories from five datasets. Our experiments show that the best results are achieved by including both our discounting and triple discrimination approaches.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-03-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 4
Issue Number 2
Page Count 29
Starting Page 1
Ending Page 29

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Source: ACM Digital Library