A Survey on Ontology Enrichment from Text

Vivek Iyer, Lalit Mohan, Mehar Bhatia, Y. Raghu Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Increased internet bandwidth at low cost is leading to the creation of large volumes of unstructured data. This data explosion opens up opportunities for the creation of a variety of data-driven intelligent systems, such as the Semantic Web. Ontologies form one of the most crucial layers of semantic web, and the extraction and enrichment of ontologies given this data explosion becomes an inevitable research problem. In this paper, we survey the literature on semi-automatic and automatic ontology extraction and enrichment and classify them into four broad categories based on the approach. Then, we proceed to narrow down four algorithms from each of these categories, implement and analytically compare them based on parameters like context relevance, efficiency and precision. Lastly, we propose a Long Short Term Memory Networks (LSTM) based deep learning approach to try and overcome the gaps identified in these approaches.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Natural Language Processing
Place of PublicationInternational Institute of Information Technology, Hyderabad, India
PublisherNLP Association of India
Number of pages10
Publication statusPublished - 18 Dec 2019
Event16th International Conference on Natural Language Processing - Hyderabad, India
Duration: 18 Dec 201921 Dec 2021


Conference16th International Conference on Natural Language Processing
Abbreviated titleICON 2019
Internet address


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