Semi-supervised instance population of an ontology using word vector embedding 机翻标题: 暂无翻译,请尝试点击翻译按钮。

会议集名/来源
2017 Seventeenth International Conference on Advances in ICT for Emerging Regions: ICTer 2017, Colombo, Sri Lanka, 6-9 September 2017
出版年
2017
页码
1-7
会议地点
Colombo
作者单位
Department of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaDepartment of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaDepartment of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaDepartment of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaDepartment of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaDepartment of Computer Science & Engineering, University of Moratuwa Bandaranayake Mawatha, Katubedda, Moratuwa, Sri LankaUniversity of London International Programmes, University of London 32 Russell Square, Bloomsbury, London, United Kingdom
作者
Vindula Jayawardana;Dimuthu Lakmal;Nisansa de Silva;Amal Shehan Perera;Keet Sugathadasa;Buddhi Ayesha;Madhavi Perera
摘要
In many modern-day systems such as information extraction and knowledge management agents, ontologies play a vital role in maintaining the concept hierarchies of the selected domain. However, ontology population has become a problematic process due to its nature of heavy coupling with manual human intervention. With the use of word embeddings in the field of natural language processing, it became a popular topic due to its ability to cope up with semantic sensitivity. Hence, in this study we propose a novel way of semi-supervised ontology population through word embeddings 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 with better accuracy. We demonstrate that our ensemble model can outperform the individual models.
机翻摘要
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关键词
Ontologies;Sociology;Statistics;Law;Natural language processing;Semantics
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