Information Security Education Journal Maleerat Maliyaem 13 1 2026 https://doi.org/10.6025/isej/2026/13/1/20-34 https://www.dline.info/isej/fulltext/v13n1/isejv13n1_2.pdf Cybersecurity education and threat intelligence increasingly require structured, interpretable frameworks to manage complex and heterogeneous security data. This study introduces AISecKG, a comprehensive cybersecurity knowledge graph dataset comprising 1,460+ entities and 726 semantic relations, designed to bridge the gap between theoretical ontology construction and practical application. Employing a layered architecture, we systematically analyze the graph's structural and semantic properties through network topology metrics, community detection, and embedding techniques. Network analysis reveals a sparse, hierarchical topology with a density of 0.0018 and an average clustering coefficient of 0.0227, indicating specialized relational patterns rather than dense interconnections. Centrality metrics identify Nmap and firewall as critical hub and bridging nodes, facilitating knowledge propagation across offensive and defensive domains. Application of the Louvain algorithm uncovers 20 distinct functional communities, ranging from network scanning to intrusion detection, confirming the graph's modular organization. Furthermore, TransE and Node2Vec embeddings successfully capture relational and structural semantics, effectively separating offensive tools, defensive mechanisms, and infrastructure components in vector space. These findings validate AISecKG's utility for downstream machine learning tasks, including entity classification and similarity search, while demonstrating its potential to enhance adaptive cybersecurity education. By transforming fragmented security data into actionable, semantically rich intelligence, this research addresses critical implementation gaps and offers a scalable, interpretable framework for both academic training and operational threat analysis.