Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances

Aurawan Imsombut and Jesada Kajornrit ()
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Jesada Kajornrit: College of Creative Design and Entertainment Technology, Dhurakij Pundit University, Bangkok, Thailand

Journal of Reviews on Global Economics, 2017, vol. 6, 375-379

Abstract: Enriching instances into an ontology is an important task because the process extends knowledge in ontology to cover more extensively the domain of interest, so that greater benefits can be obtained. There are many techniques to classify instances of concepts with two popular techniques being the statistical and data mining methods. The paper compares the use of the two methods to classify instances to enrich ontology having greater domain knowledge, and selects a conditional random field for the statistical method and feature-weight k-nearest neighbor classification for the data mining method. The experiments are conducted on tourism ontology. The results show that conditional random fields methods provide greater precision and recall value than the other, specifically, F1-measure is 74.09% for conditional random fields and 60.04% for feature-weight k-nearest neighbor classification.

Keywords: Ontology Enrichment; Statistical Technique; Classification; Conditional Random Fields (CRFs); Feature-weighted k-Nearest Neighbor. (search for similar items in EconPapers)
JEL-codes: C38 C44 C82 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:lif:jrgelg:v:6:y:2017:p:375-379

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