Summary of A Rag Approach For Generating Competency Questions in Ontology Engineering, by Xueli Pan et al.
A RAG Approach for Generating Competency Questions in Ontology Engineering
by Xueli Pan, Jacco van Ossenbruggen, Victor de Boer, Zhisheng Huang
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a novel approach to automating the formulation of competency questions (CQs) in ontology development and evaluation. Traditionally, this task relies heavily on domain experts and knowledge engineers, but with the advent of Large Language Models (LLMs), it is now possible to leverage these models for automatic generation. The proposed retrieval-augmented generation (RAG) approach uses LLMs to generate CQs from a set of scientific papers serving as a domain knowledge base. The performance of this approach is investigated, with experiments conducted using GPT-4 on two ontology engineering tasks and compared against ground-truth CQs constructed by domain experts. The results reveal that adding relevant domain knowledge to the RAG improves the performance of LLMs in generating CQs for concrete ontology engineering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us create better questions about what we know, using big language models like GPT-4. Usually, it takes a lot of work from experts to come up with these questions, but now we can use computers to help us. The researchers tried out a new way to do this, called retrieval-augmented generation (RAG), which uses the big language model to create questions based on what we already know about a topic. They tested it and found that it gets better results when we give it more information to work with. |
Keywords
» Artificial intelligence » Gpt » Knowledge base » Language model » Rag » Retrieval augmented generation
