Ontology Alignment Approach Based on Attribute Self-Adaptation Mechanism and Heterogeneous Feature Fusion

Ontology alignment is a crucial task in the field of knowledge fusion. It provides an essential basis for constructing large-scale high-quality knowledge graphs. However, the previous ontology alignment works face the three problems: lack of implicit semantic within reference mapping (i.e., labeled set), falsely high similarity between class embeddings, and imbalanced training data. To solve these problems, this paper proposes an active learning ontology alignment approach with attribute self-adaptation mechanism and heterogeneous feature fusion (ASHF). The active learning framework and attribute self-adaptation mechanism aim to avoid false positives aligned classes by reconstructing the reference mapping, and to obtain stable performance for sparse ontologies. The heterogeneous feature fusion strategy calculates the similarity between classes by selecting the more distinguished semantic features of classes. Experimental results on eight public datasets show that the proposed model outperforms the state-of-art methods, demonstrating the effectiveness and superiority of the proposed approach in this paper.