用大模型自动从评论中提取产品术语关系，省去人工构建耗时。
Meronymic Ontology Extraction via Large Language Models
- 利用大模型从原始评论中自动抽取产品部件关系（meronymies）
- 生成的本体在大模型评分下优于基于BERT的基线方法
- 为自动化本体构建提供新思路，适合电商等场景
本体在当今数字时代对组织海量非结构化文本至关重要。通过为信息提供形式化结构，本体在多个领域具有巨大价值和应用前景，例如电子商务，其中成千上万的产品列表需要合理的组织。然而，手动构建本体耗时、昂贵且费力。本文利用大语言模型（LLMs）的最新进展，提出一种完全自动化的从原始评论文本中提取产品本体（以部分-整体关系形式呈现）的方法。实验表明，该方法生成的本体在使用大模型作为裁判的评估中优于现有的基于BERT的基线方法。本研究为大模型在（产品或其他类型）本体提取中的更广泛应用奠定了基础。
原文摘要 · Abstract (English)
Ontologies have become essential in today's digital age as a way of organising the vast amount of readily available unstructured text. In providing formal structure to this information, ontologies have immense value and application across various domains, e.g., e-commerce, where countless product listings necessitate proper product organisation. However, the manual construction of these ontologies is a time-consuming, expensive and laborious process. In this paper, we harness the recent advancements in large language models (LLMs) to develop a fully-automated method of extracting product ontologies, in the form of meronymies, from raw review texts. We demonstrate that the ontologies produced by our method surpass an existing, BERT-based baseline when evaluating using an LLM-as-a-judge. Our investigation provides the groundwork for LLMs to be used more generally in (product or otherwise) ontology extraction.
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