Abstract: Cybersecurity vulnerability information is distributed across numerous platforms and databases, making it difficult for researchers and practitioners to obtain a unified and structured understanding of existing threats. This is a critical issue in cybersecurity, where timely access to accurate vulnerability information directly impacts risk assessment and decision-making. While previous work has shown that knowledge graphs are effective for organizing vulnerability data, a major research gap remains in their accessibility, as querying such graphs typically requires expertise in graph query languages like Cypher. This paper aims to address this gap by proposing an approach that combines the construction of a cybersecurity knowledge graph with natural language-based interrogation. The proposed methodology relies on data collected from the National Vulnerability Database (NVD)(1) through its REST API and models vulnerabilities, products, vendors, severity metrics, weaknesses, and references using the Labeled Property Graph paradigm in Neo4j. The knowledge graph is deployed on Neo4j Aura Cloud and queried through an AI-assisted interface that translates natural language queries into Cypher language. The key contribution of this work is demonstrating that natural language querying significantly lowers the barrier to interacting with cybersecurity knowledge graphs, enabling more intuitive exploration and analysis of vulnerability data, and thereby enhancing their practical usefulness for a broader range of users in the cybersecurity field.