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Similarity function recommender service using incremental user knowledge acquisition


Ryu, SH and Benatallah, B and Paik, H-Y and Kim, YS and Compton, P, Similarity function recommender service using incremental user knowledge acquisition, Lecture Notes in Computer Science 7084: ICSOC 2011, 5-8 December 2011, Paphos, Cyprus, pp. 219-234. ISBN 978-3-642-25534-2 (2011) [Refereed Conference Paper]

Copyright Statement

Copyright 2011 Springer

DOI: doi:10.1007/978-3-642-25535-9_15


Similar entity search is the task of identifying entities that most closely resemble a given entity (e.g., a person, a document, or an image). Although many techniques for estimating similarity have been proposed in the past, little work has been done on the question of which of the presented techniques are most suitable for a given similarity analysis task. Knowing the right similarity function is important as the task is highly domain- and data-dependent. In this paper, we propose a recommender service that suggests which similarity functions (e.g., edit distance or jaccard similarity) should be used for measuring the similarity between two entities. We introduce the notion of "similarity function recommendation rule" that captures user knowledge about similarity functions and their usage contexts. We also present an incremental knowledge acquisition technique for building and maintaining a set of similarity function recommendation rules.

Item Details

Item Type:Refereed Conference Paper
Keywords:knowledge acquisition, expert systems, similarity function, recommendation, entity search, RDR
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Kim, YS (Dr Yang Kim)
ID Code:94660
Year Published:2011
Deposited By:Information and Communication Technology
Deposited On:2014-09-15
Last Modified:2014-12-09

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