Chatbot-Based Ontology Interaction Using Large Language Models and Domain-Specific Standards
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909352363819008 |
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| author | Reif, Jonathan Jeleniewski, Tom Gill, Milapji Singh Gehlhoff, Felix Fay, Alexander |
| author_facet | Reif, Jonathan Jeleniewski, Tom Gill, Milapji Singh Gehlhoff, Felix Fay, Alexander |
| contents | The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chatbot-Based Ontology Interaction Using Large Language Models and Domain-Specific Standards Reif, Jonathan Jeleniewski, Tom Gill, Milapji Singh Gehlhoff, Felix Fay, Alexander Information Retrieval Artificial Intelligence The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research. |
| title | Chatbot-Based Ontology Interaction Using Large Language Models and Domain-Specific Standards |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2408.00800 |