Citation: Agrawal, G.; Deng, Y.; Park, J.; Liu, H.; Chen, Y.-C. Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education. Information 2022, 13, 526. https://doi.org/10.3390/ info13110526 Academic Editor: Ryutaro Ichise Received: 27 September 2022 Accepted: 2 November 2022 Published: 4 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). information Article Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education Garima Agrawal 1, * , Yuli Deng 1 , Jongchan Park 2 , Huan Liu 1 and Ying-Chih Chen 2 1 School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA 2 Mary Lou Fulton Teachers College, Arizona State University, Tempe, AZ 85281, USA * Correspondence: garima.agrawal@asu.edu 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 1. Introduction The ability to represent knowledge, visualize, manage and retrieve useful information has made knowledge graphs more popular than other traditional knowledge-oriented information management and mining systems. After Google coined the term ‘Knowledge Graph’ (KG) in 2012 [1], mainly for the purpose of web-based semantic search, knowledge graphs have undergone many technical advancements and broader applications both in industry and academia. Knowledge graphs are basically an extension of semantic nets [2], which represent knowledge in the form of interconnected nodes and arcs, where nodes represent ‘objects’ or ‘concepts’ and the arcs or edges represent the interaction or relations between them. The semantic nets lacked formal syntax and semantics, whereas knowledge graphs overcame the limitations of these methods by defining a structured schema and a formal way of interpreting the graphs. Knowledge graphs have an entity-centric view; they not only focus on the structured representation of semantic knowledge but also on how the entities are linked, interpreted and disambiguated. This helps in checking the correctness, connectedness and consistency of information. Knowledge graphs provide flexibility for the user in adding meaningful information or ‘explicit knowledge’ in the form of entity attributes and relational labels. The convention to define the scope and semantic conditions of entities and relations in a given domain is called an ontology. It is crucial to have a well-defined ontology for the construction of a domain-specific knowledge graph as the ontology enables domain entailment and drives the behavior of entities and Information 2022, 13, 526. https://doi.org/10.3390/info13110526 https://www.mdpi.com/journal/information