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Abdoulahi Boubacar
Beijing Institute of Technology

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Valuing Semantic Similarity Abdoulahi Boubacar; Zhendong Niu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Similarity is a tool widely used in various domains such as DNA sequence analysis, knowledge representation, natural language processing, data mining, information retrieval, and information flow. Computing semantic similarity between two entities is a non-trivial task. There are many ways to define semantic similarity. Some measures have been proposed combining both statistical information and lexical similarity. It is difficult for a measure that performs well in a given domain to be applied with accuracy in another domain. Similarity measure may perform better with one language than another. Word is supposed to be not only similar to itself but also to some of its synonyms in a given context and some words with common roots. Our approach is designed to perform query matching and compute semantic relatedness using word occurrences. It performs better than classical measures like TF-IDF and Cosine. Although it is not a metric, the proposed similarity measure can be used for a wide range of content analysis tasks based on semantic distance and its efficacy has been demonstrated. The measure is not corpus dependent so it can establish directly the semantic relatedness of two entities. DOI: http://dx.doi.org/10.11591/telkomnika.v12i8.6034 
Conceptual Search Based on Semantic Relatedness Abdoulahi Boubacar; Zhendong Niu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured documents. It is consequently very time consuming to annotate such big data. Concept based retrieval systems intend to manage directly unstructured documents. Semantic relationships are their main feature to extend syntactic search. In most of the methods implemented so far, concepts are used for both indexing and searching. Words remain the smallest unit to process semantic relatedness. The differences persist in the way that concepts are represented, mapped to each other, and managed for the sake of indexing and/or searching. Our approach is based on Wikipedia concepts. Concepts are represented as an undirected graph. Their semantic relatedness are computed with a distance derived from a semantic similarity measure. The same distance is used to calculate both semantic relatedness and query matching. DOI:  http://dx.doi.org/10.11591/telkomnika.v12i8.5143 
Co-Authors Zhendong Niu