Summary of Commonsense Ontology Micropatterns, by Andrew Eells et al.
Commonsense Ontology Micropatterns
by Andrew Eells, Brandon Dave, Pascal Hitzler, Cogan Shimizu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Modular Ontology Modeling methodology (MOMo) is an innovative approach to creating complex concepts by combining smaller patterns. To facilitate this process, MOMo organizes design patterns into queryable libraries, enabling accelerated development of ontologies for both humans and machines. While MOMo has shown promise, its widespread adoption is hindered by the limited availability of pre-existing ontology design patterns. Large Language Models have emerged as a reliable source of common knowledge, sometimes even replacing search engines. This paper presents a collection of 104 curated ontology design patterns representing frequently occurring nouns, extracted from LLMs and organized into a fully annotated modular ontology design library compatible with MOMo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors created a new way to build complex ideas by combining smaller parts. They made it easier for humans and computers to develop ontologies by organizing these small pieces into a special library that can be searched. However, this approach has some limitations because there aren’t many pre-made patterns available yet. Large language models have become very good at providing general knowledge and sometimes even replace search engines. In this paper, the authors share 104 examples of common ideas, taken from large language models, organized in a way that can be used with their special approach. |
