DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification
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Abstract (arXiv)
We introduce semantic towers, an extrinsic knowledge representation method, and compare it to intrinsic knowledge in large language models for ontology learning. Our experiments show a trade-off between performance and semantic grounding for extrinsic knowledge compared to a fine-tuned model intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 26 Aug 2024.
Builds on
7 of 13 references.
References · 7
- 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
- 2023LLMs4OL: Large Language Models for Ontology Learningcited by 75
- 2024From human experts to machines: An LLM supported approach to ontology and knowledge graph constructioncited by 40
- 2023Exploring Large Language Models for Ontology Alignmentcited by 26
- 2024LLMs4OM: Matching Ontologies with Large Language Modelscited by 22
- 2023Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)cited by 14
- 2024Towards Ontology-Enhanced Representation Learning for Large Language Modelscited by 3
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Citing papers per year: 2024: 1.
1 paper.