Threat analysis is continuously growing in importance due to the always-increasing complexity and frequency of cyber attacks. Analyzing threats demands significant effort from security experts: different cybersecurity knowledge bases support this task, but manual efforts are required to correlate heterogeneous sources into a unified view that would enable a more comprehensive assessment. To address this gap, we propose ThreatLinker, a methodology leveraging Natural Language Processing (NLP) to effectively and efficiently associate Common Vulnerabilities and Exposure (CVE) vulnerabilities with Common Attack Pattern Enumeration and Classification (CAPEC) attack patterns. The proposed technique combines semantic similarity with keyword analysis to improve the accuracy of association estimations. We contributed a larger dataset for CVE-CAPEC correlation, and experimental evaluations demonstrate superior performance compared to state-of-the-art models.