Entity Alignment Based on Multi-view Interaction Model in Vulnerability Knowledge Graphs

Entity alignment (EA) aims to match the same entities in different Knowledge Graphs (KGs), which is a critical task in KG fusion. EA has recently attracted the attention of many researchers, but the performance of general methods on KGs in some professional fields is not satisfactory. Vulnerability KG is a kind of KG that stores vulnerability knowledge. The text and structure information is not the same as the general KG, so the EA task faces unique challenges. First, although some vulnerabilities have a unified CVE number, in reality, the CVE number attribute value of many vulnerability entities in KG is missing. Second, vulnerability KGs often contain a large number of 1−n and n−1 relations, and general entity embedding methods may generate similar vector representations for a large number of non-identical vulnerabilities. To address the above challenges, we propose a multi-view text-graph interaction model (TG-INT) for the EA task in vulnerability KG. We use cross-lingual BERT to learn text embeddings and an optimized model called QuatAE to embed two graphs into a unified vector space. After that, we employed a multi-view interactive modeling scheme for the EA task. On the vulnerability KGs built on the vulnerability database CNNVD and CNVD, we verified the effectiveness of TG-INT. The results show that our model is not only suitable for vulnerability KGs but also achieves promising results in general KGs.