Optimizing Document Retrieval by Measurement Resemblance Between Semantic Word Methods

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Published: Mar 17, 2020
DOI: https://doi.org/10.14456/jait.2020.2
Keywords:
Ontology Keyword Metadata SKOS Term weighting

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Kamonwan Ratchatawetchakul
Department of Digital Technology, Faculty of Administative Science, Kalasin University, Thailand
Apichai Sarnthong
Department of Computer and Automation Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Thailand
Weerayut Ratchatawetchakul
Department of Management, Faculty of Management Science , Loei Rajabhat University, Thailand
Yongyut Ratchatawetchakul
Department of Business Computer, Mahasarakham Business School, Mahasarakham University, Thailand

Abstract

The aims of article to present the method for measuring semantic similarity between words. Data test are documents in the computer domain from ThaiLIS : Thai Library Integration System 50 documents and prepare a Dublin Core metadata for documentation such as Title, Keyword / Subject, Description / Abstract and  Source. Create ontology on Web Ontology Language (OWL) by the word form https://technology.in.th/thesaurus about 402 words that is thesaurus website by National Science and Technology Development Agency. This research use Dijkstra algorithm for shorted part between node to node relate by Edge and Node such as skos:prefLabel , skos:broader, skos:narrower, skos:related and skos:altLabel. The results showed that method of measuring semantic similarity between words by shortest path method proposed by the researcher shows efficiency more than   cosine similarity, accuracy of retrieval with F-measure and MRR. Cosine Similarity measurement and the results of the retrieval meet users' needs.

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How to Cite
Ratchatawetchakul, K., Sarnthong, A., Ratchatawetchakul, W., & Ratchatawetchakul, Y. (2020). Optimizing Document Retrieval by Measurement Resemblance Between Semantic Word Methods. Journal of Applied Informatics and Technology, 2(1), 19–29. https://doi.org/10.14456/jait.2020.2
Issue
Vol. 2 No. 1 (2020): January-June
Section
Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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