Authors : Ritesh Verma; Bhumi Bhoi

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/bdcn5pv3

Scribd : https://tinyurl.com/bdcrecc6

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1721

Abstract : The convergence of rules, ontology, and natural language processing (NLP) represents a pivotal domain in the realm of textual data analysis and interpretation. This comprehensive study delves into the intricate interplay among these three pillars of information processing, aiming to elucidate their collective impact on advancing our understanding and manipulation of textual data. Through a meticulous examination of the theoretical underpinnings and practical applications of rules, ontology, and NLP, we endeavor to uncover novel insights and methodologies for enhancing automated textual analysis and interpretation. By exploring the intersection of these disciplines, we seek to unravel the complexities inherent in textual data and pave the way for new horizons in information extraction and knowledge discovery. Join us on this journey as we navigate the fascinating landscape of rules, ontology, and NLP, and unravel their profound implications for the field of textual data analysis.

Keywords : Leveraging Machine Learning and NLP.

References :

1. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
2. Jurafsky, D., & Martin, J. H. (2020). Speech and Language Processing. Pearson.
3. Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05.
4. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2), 199-220.
5. Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th conference on Computational linguistics-Volume 2, 539-545.
6. Smith, B., & Welty, C. (2001). Ontology: Towards a new synthesis. In Formal ontology in information systems (pp. 2-15). ACM.
7. Dalianis, H., Hassel, M., Velupillai, S., & Small, J. (2018). Rules, machine learning, and NLP in medical and health-related text processing: a scoping review. Journal of Biomedical Semantics, 9(1), 1-14.
8. Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.
9. Resnik, P. (1999). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11, 95-130.
10. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
11. Niles, I., & Pease, A. (2001). Towards a standard upper ontology. In Proceedings of the international conference on formal ontology in information systems-Volume 2001 (pp. 2-9).
12. Hovy, E. H., Marcus, M. P., Palmer, M., Ramshaw, L. A., & Weischedel, R. M. (2006). OntoNotes: The 90% solution. In Proceedings of the human language technology conference of the NAACL, companion volume: Short papers (pp. 57-60).
13. de Bruijn, J., Martins, P., Kollia, I., van Harmelen, F., & Davis, B. (2006). The RacerPro knowledge representation and reasoning system: Language, applications and performance. Web Semantics: Science, Services and Agents on the World Wide Web, 4(4), 258-277.
14. W3C. (2014). OWL 2 Web Ontology Language Document Overview (Second Edition). Retrieved from https://www.w3.org/TR/owl2-overview/
15. Wilks, Y. (1997). Knowledge representation in natural language processing. Mind, 106(423), 439-460.