Summary of End-to-end Ontology Learning with Large Language Models, by Andy Lo et al.
End-to-End Ontology Learning with Large Language Models
by Andy Lo, Albert Q. Jiang, Wenda Li, Mateja Jamnik
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper introduces OLLM, a scalable method for building the taxonomic backbone of an ontology from scratch. Unlike previous approaches that focus on individual relations between entities, OLLM models entire subcomponents of the target ontology by finetuning a large language model (LLM) with a custom regularizer. The custom regularizer reduces overfitting on high-frequency concepts, allowing for more accurate and structurally intact ontologies. The paper proposes novel metrics to evaluate the quality of the generated ontology, using deep learning techniques to define robust distance measures between graphs. Experimental results on Wikipedia show that OLLM outperforms subtask composition methods in terms of semantic accuracy and structural integrity. Additionally, the model can be effectively adapted to new domains with a small number of training examples. The paper’s contributions include a novel approach to ontology construction, a suite of evaluation metrics, and open-source code and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand complex ideas by building a framework called an ontology. Building this framework takes a lot of human work. To make things easier, the authors use large language models to help build parts of the framework. The big idea is to create a whole structure for understanding complex concepts rather than just focusing on individual connections between ideas. The paper also introduces new ways to measure how well the generated framework matches the real one. The results show that this approach works better than others and can be adapted to different areas of study with minimal effort. The authors provide all their code and data online so other researchers can build upon their work. |
Keywords
» Artificial intelligence » Deep learning » Large language model » Overfitting
