Summary of On the Use Of Large Language Models to Generate Capability Ontologies, by Luis Miguel Vieira Da Silva et al.
On the Use of Large Language Models to Generate Capability Ontologies
by Luis Miguel Vieira da Silva, Aljosha Köcher, Felix Gehlhoff, Alexander Fay
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper explores the potential of Large Language Models (LLMs) in generating capability ontologies from natural language text inputs. By leveraging LLMs’ ability to create machine-interpretable models, engineers and ontology experts can potentially simplify the process of creating complex ontological models. The study presents a series of experiments using different prompting techniques and LLMs to generate capabilities with varying complexities. Error rates are recorded and compared across the generated ontologies. To evaluate the quality of the generated ontologies, a semi-automated approach is used, incorporating RDF syntax checking, OWL reasoning, and SHACL constraints. The promising results indicate that even complex capabilities can be represented in almost error-free ontologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called Large Language Models to help create models of things like machines or systems. These models are very important for people who make software and technology. But creating these models is hard work, so the researchers wanted to see if they could use the computers to do some of the work. They tested different ways of giving instructions to the computer and found that it was good at making models that were almost perfect! This means that in the future, people might not have to spend as much time creating these complex models. |
Keywords
» Artificial intelligence » Prompting » Syntax
