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A Hybrid Concept Learning Approach to Ontology Enrichment
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Author(s): Zenun Kastrati (Norwegian University of Science and Technology (NTNU), Norway), Ali Shariq Imran (Norwegian University of Science and Technology (NTNU), Norway)and Sule Yildirim Yayilgan (Norwegian University of Science and Technology (NTNU), Norway)
Copyright: 2018
Pages: 35
Source title:
Innovations, Developments, and Applications of Semantic Web and Information Systems
Source Author(s)/Editor(s): Miltiadis D. Lytras (American College of Greece, Greece), Naif Aljohani (King Abdulaziz University, Saudi Arabia), Ernesto Damiani (University of Milan, Italy)and Kwok Tai Chui (The Open University of Hong Kong, Hong Kong)
DOI: 10.4018/978-1-5225-5042-6.ch004
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Abstract
The wide use of ontology in different applications has resulted in a plethora of automatic approaches for population and enrichment of an ontology. Ontology enrichment is an iterative process where the existing ontology is continuously updated with new concepts. A key aspect in ontology enrichment process is the concept learning approach. A learning approach can be a linguistic-based, statistical-based, or hybrid-based that employs both linguistic as well as statistical-based learning approaches. This chapter presents a concept enrichment model that combines contextual and semantic information of terms. The proposed model called SEMCON employs a hybrid concept learning approach utilizing functionalities from statistical and linguistic ontology learning techniques. The model introduced for the first time two statistical features that have shown to improve the overall score ranking of highly relevant terms for concept enrichment. The chapter also gives some recommendations and possible future research directions based on the discussion in following sections.
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