Neighborhood Matching Entity Alignment Model for Vulnerability Knowledge Graphs

Qi Yan, Mohan Li, Yanbin Sun

Abstract: Entity alignment aims to match identical entities in different knowledge graphs (KGs). In recent years, entity alignment methods for encyclopedic KGs have achieved significant effectiveness. However, the characteristics of KGs of some specific domains differs from encyclopedic KGs, so that encyclopedic entity alignment methods do not perform well in domain KGs. Vulnerability KGs are a typical type of domain KG characterized by a large number of entities and limited structural variations, but strong heterogeneity across different graphs. The neighborhood matching-based entity alignment methods are effective on vulnerability KGs. However, previous neighborhood matching methods have primarily focused on aligning neighborhoods where both entities and relations are aligned simultaneously, neglecting the neighborhoods where only entities are aligned. Vulnerability KGs typically contain a large number of entities but have a limited types of relations. As a result, each relation often connects a significant number of entities. Incorrect matching of relations can potentially result in incorrect matching of all connected entities, leading to severe error propagation.In this paper, we propose a neighborhood matching based method VNM, for entity alignment in vulnerability KGs. VNM considers two layers of neighborhood, that is, the neighborhood that entities and relations both align and the neighborhood that only entities align. Our method not only mitigates the error propagation caused by incorrect relation matching but also leverages richer neighborhood information. Additionally, inspired by the phenomenon of semantic translation in word embeddings, we introduce a regularizer for semantic embedding of one-to-many and many-to-one relations in vulnerability KGs. Experimental results on four real-world vulnerability datasets demonstrate that our method outperforms existing methods in terms of performance.