Forensic Extraction and Representation (FEAR) Language
The Forensic Extraction and Representation is a set of languages designed for use in the field of digital forensics. The languages focus on the ability for a practicioner to represent the expert knowledge is deconstructing evidence, extracting artifacts, and expressing how the artifacts can be represented in Semantic Knowledge Graphs.
For those working in the field of digital forensics with semantic knowledge graphs, the languages enable the sharing of information regarding the construction of a knowledge graph from a digital forensic evidence dataset, removing the need to develop lengthy and bespoke implementations of proprietary systems to represent artifacts in knowledge graphs.
Graph-Codify FEAR (GFEAR)
The Graph-Codify language, GFEAR, has far reaching applications beyond digital forensics. While methods exist to translate data into knowledge graphs, these methods can be cumbersome to read and understand, and even built onto of representations that were not designed for translating data to knowledge.
GFEAR is a Fifth generation language (5GL) that is designed to facilitate the expression of logic for data translation from a variety of formats, into knowledge graph representation. The GFEAR language is currently implemented as a transpiled hybrid language-framework meaning that the language is translated into the source-code of another language (C#) and then compiled. The hybrid aspect of the language is related to the language services that are provided to support interactions with graphs and the merging of data.
Getting started
To get started, see Getting Started.