OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching

2024-09-21

arXiv.org

AI Summary

Large language models (LLMs) exhibit semantic hallucinations—such as spurious alignments, fabricated entities, and logical inconsistencies—when performing ontology matching, undermining reliability in knowledge-intensive tasks. Method: We introduce OAEI-LLM, the first LLM hallucination benchmark tailored to middle-school-level knowledge classification, extending the international OAEI standard. We formally define and annotate LLM-specific hallucination types in ontology matching, propose a hallucination-aware data construction paradigm with schema extension mechanisms, and ensure annotation reliability via multi-stage validation: human verification, rule-based consistency checks, expert review, and cross-model agreement analysis. Contribution/Results: We release a multi-domain ontology alignment benchmark with fine-grained hallucination annotations, enabling rigorous evaluation of hallucination detection methods, robust semantic alignment techniques, and trustworthy LLM-based ontology matching (LLM-OM) approaches.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Hallucinations of large language models (LLMs) commonly occur in domain-specific downstream tasks, with no exception in ontology matching (OM). The prevalence of using LLMs for OM raises the need for benchmarks to better understand LLM hallucinations. The OAEI-LLM dataset is an extended version of the Ontology Alignment Evaluation Initiative (OAEI) datasets that evaluate LLM-specific hallucinations in OM tasks. We outline the methodology used in dataset construction and schema extension, and provide examples of potential use cases.

Problem

• Large Language Models
• Ontology Alignment
• Educational Content

Innovation

• OAEI-LLM
• Ontology Matching
• Hallucination Errors