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

Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia
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Author(s): Yuncheng Jiang (South China Normal University, China)and Mingxuan Yang (South China Normal University, China)
Copyright: 2020
Pages: 23
Source title: Natural Language Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0951-7.ch069

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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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