A Knowledge Model of Digital Evidence Review Elements Based on Ontology

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

Ning Wang
Copyright: © 2017 |Pages: 9
DOI: 10.4018/IJDCF.2017070105
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As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.
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Constructing the unified description knowledge base can effectively solve the problems of data storage and fusion caused by multi-source and heterogeneous data. The familiar knowledge base model includes knowledge base model based on XML, knowledge base model based on IDMEF format, knowledge base model based on first order predicate logic, knowledge base model based on Ontology (Fagerberg, Fosaas & Sapprasert, 2012). XML lacks the ability to express and manage the semantic, and cannot be appropriate to build a semantically-rich knowledge base model. IDMEF is only directed at IDS system and the first order predicate logic cannot support to express uncertainty knowledge. The ontology knowledge base can provide the consistent expression of domain knowledge and be applied to handling the multi-source heterogeneous data.

Tom Gruber defined “ontology” as “a specification of a conceptualization”, which is a description of the concepts and relationships that can exist for an agent or a community of agents. The representational primitives are typically classes, attributes, and relationships. The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application (Gruber, 1995).

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