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Top1. 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).