@inproceedings{prakash-etal-2024-loc,
    title = "{LOC}: Livestock Ontology Construction Approach From Domain based Text Documents",
    author = "Prakash, Nandhana  and
      A, Amudhan  and
      R, Nithish  and
      Saravanan, Krithikha Sanju",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.icon-1.53/",
    pages = "454--461",
    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{\%}."
}