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    <titleInfo>
        <title>A Survey on Ontology Enrichment from Text</title>
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        <namePart type="given">Vivek</namePart>
        <namePart type="family">Iyer</namePart>
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    <name type="personal">
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        <namePart type="family">Mohan</namePart>
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    <name type="personal">
        <namePart type="given">Mehar</namePart>
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    <name type="personal">
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        <namePart type="given">Raghu</namePart>
        <namePart type="family">Reddy</namePart>
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            <title>Proceedings of the 16th International Conference on Natural Language Processing</title>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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            <namePart type="given">Pushpak</namePart>
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            <publisher>NLP Association of India</publisher>
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    <abstract>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.</abstract>
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        <date>2019-12</date>
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            <start>95</start>
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%0 Conference Proceedings
%T A Survey on Ontology Enrichment from Text
%A Iyer, Vivek
%A Mohan, Lalit
%A Bhatia, Mehar
%A Reddy, Y. Raghu
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F iyer-etal-2019-survey
%X 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.
%U https://aclanthology.org/2019.icon-1.11/
%P 95-104

Markdown (Informal)

[A Survey on Ontology Enrichment from Text](https://aclanthology.org/2019.icon-1.11/) (Iyer et al., ICON 2019)

• A Survey on Ontology Enrichment from Text (Iyer et al., ICON 2019)

ACL

• Vivek Iyer, Lalit Mohan, Mehar Bhatia, and Y. Raghu Reddy. 2019. A Survey on Ontology Enrichment from Text. In Proceedings of the 16th International Conference on Natural Language Processing, pages 95–104, International Institute of Information Technology, Hyderabad, India. NLP Association of India.