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        <title>LOC: Livestock Ontology Construction Approach From Domain based Text Documents</title>
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    <abstract>Livestock plays an irreplaceable role in rural and global economies and as a part of its progression livestock ontology would unlock its potential of cross - domain applications of Natural Language Processing (NLP). Domain data is essential for the retrieval of semantic and syntactic understanding of the input text data given to the model. The paper presents a Livestock based Ontology Construction (LOC) is proposed. The input data endures anaphora resolution employing semantic methods based on rules then the pre-trained BERT model with Regular expression are utilized for retrieving terms (entities) from the data. Now the Graph Neural Network (GNN) is constructed with Regular Expressions for extricating relationships from the input documents for designing the livestock ontology. The efficaciousness of the proposed LOC based on the BERT model with regular expressions and GNN method with Regular expressions depicts noteworthy results when compared to existing methods, showing a precision and recall of 97.56% and 95.24%.</abstract>
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%0 Conference Proceedings
%T LOC: Livestock Ontology Construction Approach From Domain based Text Documents
%A Prakash, Nandhana
%A A, Amudhan
%A R, Nithish
%A Saravanan, Krithikha Sanju
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F prakash-etal-2024-loc
%X Livestock plays an irreplaceable role in rural and global economies and as a part of its progression livestock ontology would unlock its potential of cross - domain applications of Natural Language Processing (NLP). Domain data is essential for the retrieval of semantic and syntactic understanding of the input text data given to the model. The paper presents a Livestock based Ontology Construction (LOC) is proposed. The input data endures anaphora resolution employing semantic methods based on rules then the pre-trained BERT model with Regular expression are utilized for retrieving terms (entities) from the data. Now the Graph Neural Network (GNN) is constructed with Regular Expressions for extricating relationships from the input documents for designing the livestock ontology. The efficaciousness of the proposed LOC based on the BERT model with regular expressions and GNN method with Regular expressions depicts noteworthy results when compared to existing methods, showing a precision and recall of 97.56% and 95.24%.
%U https://aclanthology.org/2024.icon-1.53/
%P 454-461

Markdown (Informal)

[LOC: Livestock Ontology Construction Approach From Domain based Text Documents](https://aclanthology.org/2024.icon-1.53/) (Prakash et al., ICON 2024)

• LOC: Livestock Ontology Construction Approach From Domain based Text Documents (Prakash et al., ICON 2024)

ACL

• Nandhana Prakash, Amudhan A, Nithish R, and Krithikha Sanju Saravanan. 2024. LOC: Livestock Ontology Construction Approach From Domain based Text Documents. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 454–461, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).