Contextualized Structural Self-supervised Learning for Ontology Matching

TL;DR

This paper introduces LaKERMap, a self-supervised learning framework for ontology matching that leverages contextual and structural information to improve alignment accuracy and reduce inference time in knowledge graphs.

Contribution

It presents a novel self-supervised framework that integrates implicit knowledge into transformers to better capture structural contexts in ontology matching.

Findings

LaKERMap outperforms state-of-the-art systems in alignment quality.

LaKERMap reduces inference time significantly.

The framework effectively captures local and global structural interactions.

Abstract

Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources. Recent advancements in deep OM models have harnessed the power of transformer-based language models and the advantages of knowledge graph embedding. Nevertheless, these OM models still face persistent challenges, such as a lack of reference alignments, runtime latency, and unexplored different graph structures within an end-to-end framework. In this study, we introduce a novel self-supervised learning OM framework with input ontologies, called LaKERMap. This framework capitalizes on the contextual and structural information of concepts by integrating implicit knowledge into transformers. Specifically, we aim to capture multiple structural contexts, encompassing both local and global interactions, by employing distinct training objectives. To assess our methods, we utilize the Bio-ML datasets and tasks. The findings from our innovative approach reveal that LaKERMap surpasses state-of-the-art systems in terms of alignment quality and inference time. Our models and codes are available here: https://github.com/ellenzhuwang/lakermap.