Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education

Abstract

Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks.

Keywords

• knowledge graph
• knowledge graph question answering systems (KGQA)
• natural language processing (NLP)
• ontology
• cybersecurity