Ontology alignment with semantic and structural embeddings

Abstract

Ontology alignment is essential for data integration and interoperability across multiple applications across diverse disciplines. In recent decades, significant advancements have been made in the development of advanced methods and systems for ontology alignment. Empirical results have suggested that ontological semantics can be effectively employed to enhance the alignment process. Besides, structural information is crucial for ontology alignment as it reflects the relations among adjacent concepts in the ontology. Previous works are mainly based on external lexicon and predefined rules based on ontological structure. Recently, deep learning has imposed positive impacts on ontology alignment and obtained substantial improvement.

This paper proposes a new method based on ontology embedding incorporating the semantic and structural features. It utilizes the distance between the embedding of two ontological concepts to be aligned as the criterion for alignment. The proposed method is used to align two widely used food ontologies and three Chinese food classification ontologies. The experimental results show that our method enhances the performance compared to several state-of-the-art alignment systems, demonstrating the importance of learning semantic representation and structural representation. Furthermore, the proposed method is evaluated on several different tracks of the Ontology Alignment Evaluation Initiative (OAEI), and experimental results show that our method outperforms other baselines in effectiveness. The data and code can be obtained from: https://github.com/haozhigang1111/Ontology-Alignment.git.