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SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts

SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts

Zenun Kastrati, Ali Shariq Imran, Sule Yildirim-Yayilgan
Copyright: © 2016 |Volume: 12 |Issue: 2 |Pages: 24
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466689534|DOI: 10.4018/IJSWIS.2016040101
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MLA

Kastrati, Zenun, et al. "SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts." IJSWIS vol.12, no.2 2016: pp.1-24. http://doi.org/10.4018/IJSWIS.2016040101

APA

Kastrati, Z., Imran, A. S., & Yildirim-Yayilgan, S. (2016). SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts. International Journal on Semantic Web and Information Systems (IJSWIS), 12(2), 1-24. http://doi.org/10.4018/IJSWIS.2016040101

Chicago

Kastrati, Zenun, Ali Shariq Imran, and Sule Yildirim-Yayilgan. "SEMCON: A Semantic and Contextual Objective Metric for Enriching Domain Ontology Concepts," International Journal on Semantic Web and Information Systems (IJSWIS) 12, no.2: 1-24. http://doi.org/10.4018/IJSWIS.2016040101

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Abstract

This paper presents a novel concept enrichment objective metric combining contextual and semantic information of terms extracted from the domain documents. The proposed metric is called SEMCON which stands for semantic and contextual objective metric. It employs a hybrid learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The metric also introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. Subjective and objective experiments are conducted in various domains. Experimental results (F1) from computer domain show that SEMCON achieved better performance in contrast to tf*idf, and LSA methods, with 12.2%, 21.8%, and 24.5% improvement over them respectively. Additionally, an investigation into how much each of contextual and semantic components contributes to the overall task of concept enrichment is conducted and the obtained results suggest that a balanced weight gives the best performance.

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