Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia

Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia

Yuncheng Jiang (South China Normal University, China) and Mingxuan Yang (South China Normal University, China)
DOI: 10.4018/978-1-7998-0951-7.ch069

Abstract

This article describes how the traditional web search is essentially based on a combination of textual keyword searches with an importance ranking of the documents depending on the link structure of the web. However, one of the dimensions that has not been captured to its full extent is that of semantics. Currently, combining search and semantics gives birth to the idea of the semantic search. The purpose of this article is to present some new methods to semantic search to solve some shortcomings of existing approaches. Concretely, the authors propose two novel methods to semantic search by combining formal concept analysis, rough set theory, and similarity reasoning. In particular, the authors use Wikipedia to compute the similarity of concepts (i.e., keywords). The experimental results show that the authors' proposals perform better than some of the most representative similarity search methods and sustain the intuitions with respect to human judgements.
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1. Introduction

The World Wide Web is the world’s most valuable information resource and has become the world’s largest database with search being the main tool that enables organisations and individuals to exploit its huge amounts of information that is freely offering (Virgilio et al., 2012). Thus, having a successful mechanism for finding and retrieving the most relevant information to a task at hand is of major importance. Traditionally, Web search is essentially based on a combination of textual keyword search with an importance ranking of the documents depending on the link structure of the Web (Fazzinga and Lukasiewicz 2010). However, one of the dimensions that has not been captured to its full extent is that of semantics. Combining search and semantics gives birth to the idea of the semantic search. Semantic search can be described in a sentence as the effort of improving the accuracy of the search process by understanding the context and limiting the ambiguity (Melo et al., 2016; Virgilio et al., 2012; Vocht et al., 2017). In fact, the issue of semantics has become the grand challenge for the next-generation World Wide Web (Jindal et al., 2014; Storey et al., 2008).

The development of semantic search is currently an extremely hot topic (Binding et al. 2015; Fazzinga and Lukasiewicz, 2010; Formica, 2012; Formica et al., 2013; Likavec et al., 2015; Melo et al., 2016; Storey et al., 2008; Virgilio et al., 2012; Vocht et al., 2017). For example, Formica (2012) showed how rough set theory (Pawlak 1991) could be employed in combination with fuzzy formal concept analysis to perform Semantic Web search and discovery of information in the web. Formica et al. (2013) pointed out that there were many proposals on semantic search and retrieval in the literature, but there was still no specific solution that clearly emerged. For this reason, they presented a proposal based on a notion that is currently gaining momentum in the field: the Information Content (IC) approach (Resnik, 1995; Jiang et al., 2017).

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