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Heuristic semantic walk for concept chaining in collaborative networks

Valentina Franzoni (Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy)
Alfredo Milani (Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy and Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 14 April 2014

224

Abstract

Purpose

In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. Collaborative concept networks over the web tend to have features such as large dimensions, high connectivity degree, dynamically evolution over the time, which represent special challenges for efficient graph search methods, since they result in huge memory requirements, high branching factors, unknown dimensions and high cost for accessing nodes. The paper aims to discuss these issues.

Design/methodology/approach

The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heuristic in order to guide the search in the collaborative network. Different search strategies, information sources and proximity measures, can be used to adapt HSW to the collaborative semantic network under consideration.

Findings

Experiments held on the Wikipedia network and Bing search engine on a range of different semantic measures show that the proposed HSW approach with weighted randomized walk strategy outperforms state-of-the-art search methods.

Originality/value

To the best of the authors' knowledge, the proposed HSW model is the first approach which uses search engine-based proximity measures as heuristic for semantic search.

Keywords

Acknowledgements

The authors thank Marco Mencacci and Paolo Mengoni, students of the Computer Science Master degree, University of Perugia, whose WikiDist Python project, developed for the course of artificial intelligence, was successfully used in this study, and Prof. Clement Leung of Hong Kong Baptist University for the useful discussions. This work was partially supported by Italian Ministry of Education, University and Research Funding of Research Projects of National Interest (PRIN 2010-11) under Grant No. 2010FP79LR_003 “Logical methods of information management”.

Citation

Franzoni, V. and Milani, A. (2014), "Heuristic semantic walk for concept chaining in collaborative networks", International Journal of Web Information Systems, Vol. 10 No. 1, pp. 85-103. https://doi.org/10.1108/IJWIS-11-2013-0031

Publisher

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Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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