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Extracting Knowledge from Testaments - An Ontology Learning Approach

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Distributed Computing and Artificial Intelligence, 20th International Conference (DCAI 2023)

Abstract

The extraction of ontologies from textual data is not a simple task. It is necessary to have specific expertise and knowledge about the ontology application domain to carry it out, and knowing how to use properly a set of sophisticated methods and techniques, requiring the use of advanced ontology learning tools. For some years, ontologies have been instruments of great importance in the process of designing and developing knowledge systems. In this paper, we present and describe the design and development of a semi-automatic system for extracting an ontology of Portuguese testaments from a set of ancient texts of the 18th century, towards the acquisition of the knowledge about the legacies of people of a Portuguese region on that period. This ontology has great interest and relevance for knowing many aspects of that time, namely linguistic, historical, religious, cultural, economic or agricultural, among others.

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References

  1. Keet, M.: An Introduction to Ontology Engineering, University of Cape Town (2018). https://people.cs.uct.ac.za/~mkeet/OEbook/. Accessed 24 Apr 2023

  2. El Kadiri, S., Terkaj, W., Urwin, E.N., Palmer, C., Kiritsis, D., Young, R.: Ontology in engineering applications. In: Cuel, R., Young, R. (eds.) FOMI 2015. LNBIP, vol. 225, pp. 126–137. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21545-7_11

    Chapter  Google Scholar 

  3. Sharma, A.: Natural language processing and sentiment analysis, in international research. J. Comput. Sci. 8(10), 237 (2021). https://doi.org/10.26562/irjcs.2021.v0810.001

    Article  Google Scholar 

  4. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. WIREs Data Min. Knowl. Disc. 8(4) (2018). https://doi.org/10.1002/widm.1253

  5. Alves, A., Barros, A.: O Livro dos Testamentos - Picote 1780–1803. Traços do português e do mirandês setecentistas na língua jurídica. Frauga, Picote (2019). ISBN 978–989–99411–8–2

    Google Scholar 

  6. Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 1–17. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_0

    Chapter  Google Scholar 

  7. Cimiano, P., Mädche, A., Staab, S., Völker, J.: Ontology learning. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 245–267. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_11

    Chapter  Google Scholar 

  8. Asim, M., Wasim, M., Khan, M., Mahmood, W., Abbasi, H.: A survey of ontology learning techniques and applications. Database 2018 (2018). https://doi.org/10.1093/database/bay101

  9. El Ghosh, M., Naja, H., Abdulrab, H., Khalil, M.: Ontology learning process as a bottom up strategy for building domain-specific ontology from legal texts. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence, pp. 473–480. SCITEPRESS - Science and Technology Publications (2017). https://doi.org/10.5220/0006188004730480

  10. Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the Fourteenth International Conference on Computational Linguistics, Nantes France (1992). https://doi.org/10.3115/992133.992154

  11. Wong, W., Liu, W., Bennamoun, M.: Ontology learning from text. ACM Comput. Surv. 44(4), 1–36 (2012). https://doi.org/10.1145/2333112.2333115

    Article  MATH  Google Scholar 

  12. Jaiswal, S.: Natural Language Processing — Dependency Parsing. Towards Data Science. Industrial Data (2021). https://towardsdatascience.com/natural-language-processing-dependency-parsing-cf094bbbe3f7

  13. Nunes, J., Belo, O., Barros, A.: Mining ancient medicine texts towards an ontology of remedies - a semi-automatic approach. In: Proceedings of the 1st International Conference on Intelligent systems and Machine Learning (ICISML 2022), Hyderabad, India, 16–17 December (2022)

    Google Scholar 

  14. Hazman, M., El-Beltagy, S.R., Rafea, A.: A survey of ontology learning approaches. In: CEUR Workshop Proceedings, pp. 36–43 (2008)

    Google Scholar 

  15. Honnibal, M., Montani, I.: spaCy Industrial-strength Natural Language Processing in Python (2017)

    Google Scholar 

  16. Neo4J: Neo4J Graph Data Platform (2023). https://neo4j.com/. Accessed 24 Apr 2023

Download references

Acknowledgements

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and the PhD grant: 2022.12728.BD.

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Correspondence to Orlando Belo .

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Yusupov, S., Barros, A., Belo, O. (2023). Extracting Knowledge from Testaments - An Ontology Learning Approach. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_26

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