Measuring semantic similarity between digital forensics terminologies using web search engines

Abstract

Semantic similarity between different terminologies is becoming a generic problem that extends across numerous domains, touching applications developed for computational linguistics, artificial intelligence, cognitive science and, in the case of this paper, digital forensics. Despite the usefulness of semantic similarity measures in different domains, accurately measuring semantic similarity between any two terms remains a challenging task. The main difficulty lies in developing a computational method with the ability to generate satisfactory results close to how human beings perceive these terminologies, especially when used in their domain of expertise.This paper presents a novel approach of using the Web to measure semantic similarity between two terms x and y in the digital forensics domain. The proposed approach is based on the Euclidean distance, a mathematical concept used to calculate the distance between two points. This paper also shows how computing the absolute value of the difference of the logarithms of the hit count percentages of any given terms x and y relates to the computed Euclidean distance of x and y. Percentages are computed from the total number of hit counts reported by any Web search engine for the search terms x, y and the logical x AND y together. Finally, these concepts are used to deduce a formula to automatically calculate a semantic similarity measure coined as the Digital Forensic Absolute Semantic Similarity Value of the terms x and y, denoted as DFASSV(x, y).Experiments conducted using the proposed DFASSV method focuses on the digital forensics domain. However, a comparison of the DFASSV approach with previously proposed Web-based semantic similarity measures shows that this approach is well suited for digital forensics domain terminologies. In the authors' opinion however, the DFASSV approach can be applied in other domains as well because it does not require any humanannotated knowledge. DFASSV is a novel approach to semantic similarity measure and constitutes the main contribution of this paper.

100 Popular Open-Source Infosec Tools

“…Weiss measured opensource project success by counting web pages and project references [34]. Web searches have been used to find correlations between various terms and concepts [26], even using forensics terms [32].…”

Building Ontologies for Digital Forensic Terminologies

“…New meanings sometimes are assigned to existing terminologies [3]. Therefore, approaches need to be developed in digital forensics with the ability to effectively assist investigators in comprehending the meaning of new terminologies that may crop up as a result of technological change or domain evolution.…”

“…Such approaches will further assist in establishing an effective digital investigation process. Furthermore, the requirement for such approaches and in digital forensics is exceptionally important both for the advancement of the field as well as for the effective use of different domain terminologies and the representation of domain information in any court of law [3]. The next subsection now introduces the concept of ontologies.…”

“…Research by Karie and Venter [3] also proposed a mechanism for measuring semantic similarity between digital forensics terminologies using web search engines. In their paper, the authors argues that semantic similarity between different terminologies is becoming a generic problem that extends across numerous domains, touching applications developed for computational linguistics, artificial intelligence, cognitive science and, in the case of this paper, digital forensics.…”