Abstract - Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances

Journal of Reviews on Global Economics

Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances
Pages 375-379

Aurawan Imsombut and Jesada Kajornrit

DOI: https://doi.org/10.6000/1929-7092.2017.06.39

Published: 09 June 2017 


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.

Download

Submit to FacebookSubmit to Google PlusSubmit to TwitterSubmit to LinkedIn

OUR STRENGTHS

♦  Worldwide readership
♦  High quality content
♦  Maximum visibility
♦  Efficient publishing
♦  Optional Open Access

Publish your Research