Abstract
The previously introduced Modular Ontology Modeling methodology (MOMo) attempts to mimic the human analogical process by using modular patterns to assemble more complex concepts. To support this, MOMo organizes ontology design patterns (ODPs) into design libraries, which are programmatically queryable. However, a major bottleneck to large-scale deployment of MOMo is the (to-date) limited availability of ready-to-use ODPs. At the same time, Large Language Models (LLMs) have quickly become a source of common knowledge and, in some cases, replacing search engines for questions. In this paper, we thus present a collection of 104 ODPs representing often occurring nouns, curated from the common-sense knowledge available in LLMs, organized into a fully-annotated modular ontology design library ready for use with MOMo.
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Acknowledgement
The authors acknowledge partial funding by the National Science Foundation under grant 2333532 “Proto-OKN Theme 3: An Education Gateway for the Proto-OKN.”
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A Appendix
A Appendix
The 104 nouns conceptualized into our commonsense micropatterns
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Eells, A., Dave, B., Hitzler, P., Shimizu, C. (2024). Commonsense Ontology Micropatterns. In: Besold, T.R., d’Avila Garcez, A., Jimenez-Ruiz, E., Confalonieri, R., Madhyastha, P., Wagner, B. (eds) Neural-Symbolic Learning and Reasoning. NeSy 2024. Lecture Notes in Computer Science(), vol 14980. Springer, Cham. https://doi.org/10.1007/978-3-031-71170-1_6
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