Abstract
Traditional Information Retrieval systems (keyword-based search) suffer several problems. For instance, synonyms or hyponym are not taken into consideration when retrieving documents that are important for a user’s query. This study adopts an ontology of computer science and proposes an ontology indexing weight based on Wu and Palmer’s edge counting measure for solving this problem. This paper used the N-grams method for computing a family of word similarity. The study also compares the subsumption weight between Hliaoutakis and Nicola’s weight and query keywords (Decision Making, Genetic Algorithm, Machine Learning, Heuristic). A probability value (p-values) from the t-test (p = 0.105) is higher 0.05 and indicates no significant evidence, of not differences between both weights methods. The experimental results show that the document similarity score between a user’s query and the paper suggests that the new measures were effectively ranked.
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Boonyoung, T., Mingkhwan, A. (2014). Semantic Search Using Computer Science Ontology Based on Edge Counting and N-Grams. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_28
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DOI: https://doi.org/10.1007/978-3-319-06538-0_28
Publisher Name: Springer, Cham
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