How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?

The classic text preprocessing pipeline, comprising Tokenisation, Normalisation, Stop Words Removal, and Stemming/Lemmatisation, has been implemented in many systems for syntactic ontology matching (OM). However, the lack of standardisation in text preprocessing creates diversity in mapping results. In this paper, we investigate the effect of the text preprocessing pipeline on syntactic OM in 8 Ontology Alignment Evaluation Initiative (OAEI) tracks with 49 distinct alignments. We find that Phase 1 text preprocessing (Tokenisation and Normalisation) is more effective than Phase 2 text preprocessing (Stop Words Removal and Stemming/Lemmatisation). We propose two novel approaches to repair unwanted false mappings caused by Phase 2 text preprocessing. One is an ad hoc logic-based repair approach that employs an ontology-specific check to find common words that cause false mappings. These words are stored in a reserved word set and applied before the text preprocessing. By leveraging the power of large language models (LLMs), we also propose a post hoc LLM-based repair approach. This approach utilises the strong background knowledge provided by LLMs to repair non-existent and counter-intuitive false mappings after the text preprocessing. It also overcomes the tendency towards unstable true mappings by injecting the classic text preprocessing pipeline via function calling. The experimental results show that these two approaches can improve the matching correctness and the overall matching performance.