Paper #5

CNPMap: A Novel Approach for Ontology Alignment Capturing Beyond-Neighbourhoods Semantic Similarities

Abderrahmane Messous and Fatiha Barigou

Abstract: Ensuring semantic interoperability between heterogeneous systems remains a challenging task due to the structural complexity and diversity of ontological representations. Traditional ontology alignment methods often focus on local features, overlooking important semantic relationships beyond direct neighbourhoods. Here, we introduce CNPMap, a novel alignment approach that addresses this limitation by capturing non-local semantic similarities using a critical node-based partitioning strategy. CNPMap operates in three stages. First, it generates an initial alignment using a hybrid linguistic similarity measure. Then, a graph-based partitioning method exploits the Critical Node Detection Problem (CNDP) to divide ontologies into semantically coherent components. Finally, a context-aware similarity enhancement phase refines the alignments using a sigmoid function that modulates similarities based on both partition-level and entity-level relationships. We evaluated CNPMap on the OAEI 2023 Conference track. The approach improved the F-measure on several ontology pairs by 3% to 6% compared to baseline lexical matchers. For instance, the F-measure increased from 0.69 to 0.74 on the cmt–conference pair and from 0.76 to 0.82 on the cmt–sigkdd pair. CNPMap also achieved a precision of 0.75, outperforming most participating systems. However, its recall was slightly lower due to the conservative threshold used during the initial alignment phase. Our study reveals that integrating partition-based context into similarity computation significantly improves alignment quality, especially for complex ontologies. Future enhancements will focus on improving recall through adaptive thresholds and learning-based parameter tuning.

Keywords: CNPMap; Critical Nodes Detection Problem; Graph matching; Ontology matching; Ontology partitioning; Similarity enhancement.