KAE: A Property-based Method for Knowledge Graph Alignment and Extension
This work addresses the semantic heterogeneity problem in knowledge graphs, offering a more robust solution for aligning and extending KGs in domains where label matching is unreliable, though it appears incremental in its methodological advancement.
The paper tackles the problem of knowledge graph alignment and extension by introducing a property-based alignment method that does not rely on entity type labels, which often perform poorly or are inapplicable. Experimental results demonstrate the validity and superiority of the proposed framework over state-of-the-art methods, with quantitative and qualitative improvements.
A common solution to the semantic heterogeneity problem is to perform knowledge graph (KG) extension exploiting the information encoded in one or more candidate KGs, where the alignment between the reference KG and candidate KGs is considered the critical procedure. However, existing KG alignment methods mainly rely on entity type (etype) label matching as a prerequisite, which is poorly performing in practice or not applicable in some cases. In this paper, we design a machine learning-based framework for KG extension, including an alternative novel property-based alignment approach that allows aligning etypes on the basis of the properties used to define them. The main intuition is that it is properties that intentionally define the etype, and this definition is independent of the specific label used to name an etype, and of the specific hierarchical schema of KGs. Compared with the state-of-the-art, the experimental results show the validity of the KG alignment approach and the superiority of the proposed KG extension framework, both quantitatively and qualitatively.