Articles
Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population
Authors:
Vindula Jayawardana ,
University of Moratuwa, LK
About Vindula
Department of Computer Science & Engineering
Dimuthu Lakmal,
University of Moratuwa, LK
About Dimuthu
Department of Computer Science & Engineering
Nisansa de Silva,
University of Moratuwa, LK
About Nisansa
Department of Computer Science & Engineering
Amal Shehan Perera,
University of Moratuwa, LK
About Amal Shehan
Department of Computer Science & Engineering
Keet Sugathadasa,
University of Moratuwa, LK
About Keet
Department of Computer Science & Engineering
Buddhi Ayesha,
University of Moratuwa, LK
About Buddhi
Department of Computer Science & Engineering
Madhavi Perera
University of London, GB
About Madhavi
University of London International Programmes
Abstract
An ontology defines a set of representational primitives which model a domain of knowledge or discourse. With the arising fields such as information extraction and knowledge management, the role of ontology has become a driving factor of many modern day systems. Ontology population, on the other hand, is an inherently problematic process, as it needs manual intervention to prevent the conceptual drift. The semantic sensitive word embedding has become a popular topic in natural language processing with its capability to cope with the semantic challenges. Incorporating domain specific semantic similarity with the word embeddings could potentially improve the performance in terms of semantic similarity in specific domains. Thus, in this study, we propose a novel way of semi-supervised ontology population through word embeddings and domain specific semantic similarity as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model which outperformed the candidate models by 33% in comparison to the best performed candidate model.
How to Cite:
Jayawardana, V., Lakmal, D., de Silva, N., Perera, A.S., Sugathadasa, K., Ayesha, B. and Perera, M., 2018. Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population. International Journal on Advances in ICT for Emerging Regions (ICTer), 11(1), pp.1–10. DOI: http://doi.org/10.4038/icter.v11i1.7191
Published on
09 Aug 2018.
Peer Reviewed
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