|Author||Song, Dezhao ♦ Heflin, Jeff|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|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.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Journal of Data and Information Quality (JDIQ)|
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