研究文本预处理对本体匹配的影响，提出修复错误映射的新方法。
How Does A Text Preprocessing Pipeline Affect Ontology Matching?
- 分两阶段分析预处理：分词与归一化比停用词和词干化更有效。
- 在49组映射中，新方法显著提升匹配正确率和整体性能。
- 提出基于逻辑和大模型的修复方案，适合本体对齐研究者使用。
经典的文本预处理流程（分词、归一化、停用词移除、词干化/词形还原）广泛应用于本体匹配系统中，但缺乏标准化导致结果差异。本文针对8个OAEI评测赛道中的49组本体对齐，研究预处理流程的影响。发现分词与归一化（第一阶段）比停用词移除与词干化/词形还原（第二阶段）更有效。针对第二阶段产生的错误映射，提出两种修复方法：一种是预处理前基于本体特性的逻辑检查，识别引发错误映射的共现词；另一种是后处理的大语言模型（LLM）方法，利用其背景知识修复不存在或反直觉的错误映射。实验表明，两种方法均能显著提升匹配准确性和整体性能。
原文摘要 · Abstract (English)
The classical text preprocessing pipeline, comprising Tokenisation, Normalisation, Stop Words Removal, and Stemming/Lemmatisation, has been implemented in many systems for ontology matching (OM). However, the lack of standardisation in text preprocessing creates diversity in the mapping results. In this paper, we investigate the effect of the text preprocessing pipeline on 8 Ontology Alignment Evaluation Initiative (OAEI) tracks with 49 distinct alignments. We find that Tokenisation and Normalisation (categorised as Phase 1 text preprocessing) are more effective than Stop Words Removal and Stemming/Lemmatisation (categorised as Phase 2 text preprocessing). We propose two novel approaches to repair unwanted false mappings that occur in Phase 2 text preprocessing. One is a pre hoc logic-based repair approach used before text preprocessing, employing an ontology-specific check to find common words that cause false mappings. The other repair approach is the post hoc large language model (LLM)-based approach, used after text preprocessing, which utilises the strong background knowledge provided by LLMs to repair non-existent and counter-intuitive false mappings. The experimental results indicate that these two approaches can significantly improve the matching correctness and the overall matching performance.
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