CECKG: a Credible Entity Classification Method for Knowledge Graph

Knowledge Graphs (KGs), with their rich semantics, friendly structure, are crucial for enhancing AI systems' capability for understanding, reasoning, and cross-domain applications. However, KGs often face limitations in scale and quality, exhibiting incompleteness in not only missing relations (targeted by link prediction), but also missing semantic class information for numerous entities which is equally critical for schema-level semantics and downstream reasoning. There are two main issues in KG entity classification. First, common feature representation learning methods possess a ‘black box’ nature, undermining the inherent interpretability and complex semantic structure of KGs. Second, despite many studies addressing the importance of difficulty information of each instance for enhancing classifier performance, existing models often treat all entities uniformly, ignoring the impact of varying classification difficulty on the learning process. To address these issues, we propose a credible entity classification method for KG based on classification difficulty of entities, named CECKG. In this method, we first introduce an interpretable entity feature representation technique to preserve the original semantics of KGs, rather than directly mapping entity features to a low or high dimensional vector space. Moreover, to achieve credible entity classification in KGs, we incorporate the assessment idea of degree of credibility (Cr) into the design of CECKG, creating a progressive ensemble learning model that transitions from easy to difficult. The CECKG model focuses not only on the overall classification performance but also on the credibility of each entity's prediction. To reduce the computational cost of the classification difficulty of entities, a low-cost alternative is also proposed based on the intrinsic structural properties of KGs. A series of experimental results on five public KG datasets show that our proposed method outperforms fourteen state-of-the-art entity classification models in terms of accuracy and Cr.