DSTI at LLMs4OL 2024 Task A: Intrinsic Versus Extrinsic Knowledge for Type Classification

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's intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge.

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Publication Details

Journal
Open Conference Proceedings
Published
2024-10-02
DOI
https://doi.org/10.52825/ocp.v4i.2492
Citations
2
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.30
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article

DSTI at LLMs4OL 2024 Task A: Intrinsic Versus Extrinsic Knowledge for Type Classification

Hanna Abi Akl
2 citations
Open Conference Proceedings
Natural Language Processing Techniques
0.30
article

DSTI at LLMs4OL 2024 Task A: Intrinsic Versus Extrinsic Knowledge for Type Classification

Hanna Abi Akl
article en
2 citations

Abstract

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's intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge.

Open Conference ProceedingsVol. 4
Université Côte d'Azur (FR), Observatoire de la Côte d’Azur (FR)
Quality Education
Openalex Percentile: Top 49%
Natural Language Processing Techniques
0.30
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