## 1 Introduction  
As the application of computer networks becomes more widespread, cybersecurity issues are posing a serious threat to people's work and life. In recent years, cybersecurity attacks such as denial of service (DoS), phishing, and others have increased. Realizing cyberspace security situational awareness [1] by using existing cyber-threat knowledge is of great significance. Vulnerabilities are critical to cybersecurity, as they can allow hackers to gain unauthorized access or destroy computer systems. Hackers take advantage of vulnerabilities as soon as they discover them. This results in enormous losses, because individuals and enterprises are not be able to use effective protective measures immediately. Therefore, comprehensive vulnerability information can help individuals and enterprises to reduce cybersecurity risks. Many cyberattacks are implemented against software or system weaknesses. Software reliability is often affected by a variety of complex factors [2]. Mastering software weakness information can help individuals and enterprises to understand the possible cyberattacks they may encounter. Individuals and enterprises can use effective preventive measures to reduce potential losses by mastering the attack patterns of hackers. In the process of dealing with cyber-threats, people have accumulated much valuable experience. To better address existing cybersecurity threats, the MITRE Corporation maintains the CVE (common vulnerability and exposure), CWE (common weakness enumeration), and CAPEC (common attack pattern enumerations and classifications) cybersecurity knowledge databases for describing cybersecurity threats in real-worldscenarios. CVE contains a variety of computer security vulnerabilities that have been disclosed publicly, while CWE contains a number of software defect descriptions and classifications and is a community-developed list of software and hardware weakness types. CAPEC provides a public classification of common attack patterns used by hackers, and has become an important public standard for the software community to share attack patterns. These cybersecurity knowledge databases contain important empirical information. For example, CWE-266 (incorrect privilege assignment) may be threatened by CVE-2005-2741 (product allows users to grant themselves certain rights that can be used to escalate privileges). CAPEC-234 (hijacking a privileged process) can follow CAPEC-242 (code injection). Despite the fact that existing cyber-knowledge databases are already very large, new vulnerabilities, software weaknesses, and cyberattack patterns continue to emerge. At the same time, important relationships in existing knowledge databases have been discovered. For example, the mapping of CAPEC-698 (install malicious extension) to CWE-507 (trojan horse) was not added until CAPEC List Version 3.8, while CWE-1357 (reliance on uncontrolled component) was not added until CWE List Version 4.7. This is an important reason knowledge databases must be constantly updated and maintained. In recent years, knowledge graph technology has developed rapidly. A knowledge graph is a semantic network composed of entities, relationships, and their related attributes. It has been widely used in the medical [3], financial [4], and transportation fields [5], among others [6; 7; 8; 9]. However, due to the variety of network states and low data value density, the development of cybersecurity knowledge graphs remains in its infancy. Introducing knowledge graphs to the field of cybersecurity can make effective use of previous experiences [10], help to show the cybersecurity situation, and support security decision making and early warning predictions. Because people have limited cognitive abilities, the existing cybersecurity knowledge is clearly not flawless. To address this problem, we propose a model for cybersecurity knowledge graph completion. This model can infer unknown facts from existing facts to realize and complement existing cybersecurity knowledge.