用大模型自动补全知识图谱中类之间的互斥关系
Enriching Ontologies with Disjointness Axioms using Large Language Models
- 通过提示工程激发大模型隐含知识，识别类间互斥关系
- 在DBpedia上验证有效，显著减少人工标注需求
- 结合子类关系优化逻辑一致性，适合知识工程开发者
本研究探索使用大语言模型（LLMs）为本体补充类之间显式互斥断言的潜力。尽管互斥声明对知识图谱的复杂推理与一致性检查极为重要，但现有本体普遍缺失此类信息。我们提出一种方法，利用提示工程引导开放源码大模型识别类间的互斥关系，并在DBpedia本体上验证了该方法的有效性。结果表明，在合理提示策略下，大模型可可靠识别互斥关系，大幅降低手动标注成本。为进一步提升效率并保证逻辑一致性，我们设计了一个考虑子类关系的递进式填充流程，有效减少对大模型的调用次数。该工作为大模型在本体自动增强中的应用奠定基础，并揭示了提示策略优化对性能的关键影响。代码已开源。
原文摘要 · Abstract (English)
Ontologies often lack explicit disjointness declarations between classes, despite their usefulness for sophisticated reasoning and consistency checking in Knowledge Graphs. In this study, we explore the potential of Large Language Models (LLMs) to enrich ontologies by identifying and asserting class disjointness axioms. Our approach aims at leveraging the implicit knowledge embedded in LLMs, using prompt engineering to elicit this knowledge for classifying ontological disjointness. We validate our methodology on the DBpedia ontology, focusing on open-source LLMs. Our findings suggest that LLMs, when guided by effective prompt strategies, can reliably identify disjoint class relationships, thus streamlining the process of ontology completion without extensive manual input. For comprehensive disjointness enrichment, we propose a process that takes logical relationships between disjointness and subclass statements into account in order to maintain satisfiability and reduce the number of calls to the LLM. This work provides a foundation for future applications of LLMs in automated ontology enhancement and offers insights into optimizing LLM performance through strategic prompt design. Our code is publicly available on GitHub at https://github.com/n28div/llm-disjointness.
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