Using proximity to compute semantic relatedness in RDF graphs

Jose Paulo Leal1

  1. CRACS & INESC-Porto LA, Faculty of Sciences, University of Porto
    Porto, Portugal
    zp@dcc.fc.up.pt

Abstract

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terns in RDF graphs based on the notion of proximity. It proposes a formal definition of proximity in terms of the set paths connecting two concept nodes, and an algorithm for finding this set and computing proximity with a given error margin. This algorithm was implemented on a tool called Shakti that extracts relevant ontological data for a given domain from DBpedia – a community effort to extract structured data from the Wikipedia. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are also reported.

Key words

semantic similarity, semantic relatedness, ontology generation, web recommendation, processing Wikipedia data

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS121130060L

Publication information

Volume 10, Issue 4 (October 2013)
Special Issue on Advances in Model Driven Engineering, Languages and Agents
Year of Publication: 2013
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Leal, J. P.: Using proximity to compute semantic relatedness in RDF graphs. Computer Science and Information Systems, Vol. 10, No. 4, 1727-1746. (2013)