Abstract

The study of physics plays a fundamental role in understanding the natural world and its complex phenomena. To support accurate representation and computation of physics knowledge, digital ontologies have been developed. This paper presents a comprehensive physics ontology, PhyOnto, which captures domain knowledge related to units and measurement tools – critical elements in physics education and calculation. The ontology is constructed using the Web Ontology Language (OWL) within Protégé, following the “101 method” for ontology development. A series of rigorous evaluations, including taxonomy assessment, competency question validation, and content review, were conducted to establish its reliability and completeness. PhyOnto demonstrates strong potential in supporting physics education and question-answering systems by enabling structured knowledge representation for calculation-based reasoning.

References

• Thukral, A., Dhiman, S., Meher, R., & Bedi, P. (2023). Knowledge graph enrichment from clinical narratives using NLP, NER, and biomedical ontologies for healthcare applications. International Journal of Information Technology, 15(1), 53–65. https://doi.org/10.1007/s41870-022-01145-y
• Tran, A. C., Tran, T. K., Nhut, N. M., & Long, N. H. V. (2023). Building a deep ontology-based herbal medicinal plant search system. International Journal of Information Technology, 15(4), 2209–2223. https://doi.org/10.1007/s41870-023-01250-6
• Bahadorani, B., & Zaeri, A. (2020). A method for using temporal reasoners to answer the history of science questions. International Journal of Information Technology, 12(1), 181–188. https://doi.org/10.1007/s41870-019-00373-z
• Kaur, N., & Aggarwal, H. (2021). Query reformulation approach using domain specific ontology for semantic information retrieval. International Journal of Information Technology, 13(5), 1745–1753. https://doi.org/10.1007/s41870-020-00464-2
• Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology.
• McGuinness DL and Van Harmelen F. OWL web ontology language overview. W3C recommendation 2004; 10(10): 2004.
• Musen MA. The protégé project: a look back and a look forward. AI matters 2015; 1(4): 4–12.
• Collins JB and Clark D. Towards an ontology of physics. Naval Research Lab Washington DC, 2014.
• Lewis PJ. Quantum ontology: A guide to the metaphysics of quantum mechanics. Oxford University Press, 2016.
• Cvjetkovic V. Web physics ontology: online interactive symbolic computation in physics. In: 2017 4th Experiment@ International Conference (exp. at'17), June 2017, pp. 52-57, IEEE.
• Uschold M and King M. Towards a methodology for building ontologies. University of Edinburgh, 1995.
• Grüninger M and Fox MS. Methodology for the design and evaluation of ontologies, https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.8723 (1995, accessed 21 August 2022).
• Choy CS, Chuan KK, Beng OH, Mustafa MKAM and Ragavan R. Physics Form 4. Sasbadi Sdn Bhd, 2019.
• Chuan KK, Choy CS, Bongkek NR, Kasron J, Mustafa MKAM and Chakrabarty PK. Physics Form 5. Penerbit Bestari Sdn Bhd, 2020.
• Knublauch H, Horridge M, Musen MA, Rector AL, Stevens R, Drummond N, Lord PW, Noy NF, Seidenberg J, Wang H. The Protege OWL Experience. In Proceedings of the OWLED*05 Workshop on OWL: Experiences and Directions, 11-12 November 2005, Galway, Ireland.
• Fernández-López M, Gómez-Pérez A and Juristo N. Methontology: from ontological art towards ontological engineering. In: Proceedings of the Ontological Engineering AAAI-97 Spring Symposium Series, 24-26 March 1997, Stanford University, EEUU.
• Gómez-Pérez A. Ontology evaluation. In Handbook on ontologies. Springer, Berlin, Heidelberg., 2004, pp. 251-273.
• Vrandečić D. Ontology evaluation. In Handbook on ontologies. Springer, Berlin, Heidelberg., 2009, pp. 293-313.
• Glimm B, Horrocks I, Motik B and Stoilos G. Optimising ontology classification. In: International Semantic Web Conference, November 2010, pp. 225-240. Springer, Berlin, Heidelberg.