A Bootstrapping Approach for Developing a Cyber-security Ontology Using Textbook Index Terms

Arwa M. Wali, Soon Ae Chun, J. Geller
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引用次数: 21

Abstract

Developing a domain ontology with concepts and relationships between them is a challenge, since knowledge engineering is a labor intensive process that can be a bottleneck and is often not scalable. Developing a cyber-security ontology is no exception. A security ontology can improve search for security learning resources that are scattered in different locations in different formats, since it can provide a common controlled vocabulary to annotate the resources with consistent semantics. In this paper, we present a bootstrapping method for developing a cyber-security ontology using both a security textbook index that provides a list of terms in the security domain and an existing security ontology as a scaffold. The bootstrapping approach automatically extracts the textbook index terms (concepts), derives a relationship to a concept in the security ontology for each and classifies them into the existing security ontology. The bootstrapping approach relies on the exact and approximate similarity matching of concepts as well as the category information obtained from external sources such as Wikipedia. The results show feasibility of our method to develop a more comprehensive and scalable cyber-security ontology with rich concepts from a textbook index. We provide criteria used to select a scaffold ontology among existing ontologies. The current approach can be improved by considering synonyms, deep searching in Wikipedia categories, and domain expert validation.
利用教科书索引术语开发网络安全本体的自举方法
开发具有概念和它们之间关系的领域本体是一个挑战,因为知识工程是一个劳动密集型过程,可能成为瓶颈,并且通常不可扩展。开发网络安全本体也不例外。安全本体可以改进以不同格式分散在不同位置的安全学习资源的搜索,因为它可以提供一个通用的受控词汇表,用一致的语义对资源进行注释。在本文中,我们提出了一种开发网络安全本体的自举方法,该方法使用提供安全领域术语列表的安全教科书索引和现有的安全本体作为框架。自引导方法自动提取教科书索引术语(概念),为每个概念派生出与安全本体中概念的关系,并将它们分类到现有的安全本体中。自举方法依赖于概念的精确和近似相似性匹配以及从外部来源(如Wikipedia)获得的类别信息。结果表明,该方法可以从教科书索引中开发出更全面、更可扩展的概念丰富的网络安全本体。我们提供了用于在现有本体中选择支架本体的标准。目前的方法可以通过考虑同义词、维基百科分类中的深度搜索和领域专家验证来改进。
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