Heuristic semantic walk for concept chaining in collaborative networks

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.