Title: Term-specific eigenvector-centrality in multi-relation networks

Authors: François Bry; Fabian Kneissl; Klara Weiand; Tim Furche

Addresses: Institute for Informatics, Ludwig-Maximilian University of Munich, 80538 Munich, Germany ' Institute for Informatics, Ludwig-Maximilian University of Munich, 80538 Munich, Germany ' Institute for Informatics, Ludwig-Maximilian University of Munich, 80538 Munich, Germany ' Department of Computer Science, Oxford University, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK

Abstract: Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index time.

Keywords: approximate matching; structured data; eigenvector centrality; indexing methods; keyword search; social networks; semantic web; PageRank; multi-relation networks; fuzzy matching; fuzzy ranking; information retrieval; web search; structured data; multi-relation graphs; eigenvectors; term distribution; document structure; document retrieval.

DOI: 10.1504/IJSNM.2012.051055

International Journal of Social Network Mining, 2012 Vol.1 No.2, pp.141 - 159

Received: 31 Jan 2012
Accepted: 10 Feb 2012
Published online: 21 Aug 2014 *

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