KAE: A Property-based Method for Knowledge Graph Alignment and Extension

TL;DR

This paper introduces KAE, a novel property-based method for aligning and extending knowledge graphs that overcomes limitations of label-based approaches by focusing on properties defining entity types.

Contribution

The paper presents a machine learning framework that uses property-based alignment for knowledge graph extension, offering a more robust alternative to traditional entity type label matching.

Findings

Property-based alignment outperforms label-based methods in accuracy.

The framework effectively extends knowledge graphs with improved semantic consistency.

Experimental results validate the approach's superiority over state-of-the-art methods.

Abstract

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.