Domain Ontology of Hand-drawn Avatars as Online Self-representations for Cyber Forensics

Abstract

The idea of concepts and relationships in the organization of hand-drawn avatars can be defined and identified with ontology. Hand-drawn avatars as online self-representation can be used for criminal investigation in cyber space. These iconic self- representations are important as supporting evidence for other physical evidence in forensic investigation. Informal knowledge about avatars as online self-representation is acquired by considering the broadest possible categories of hand-drawn avatars among 210 participants of ages between 21 and 22 years old with no prior knowledge of readily available online avatars. An analysis of an earlier research yields nine categories of the avatars: inanimate object, cartoon, humanoid, male figure, female figure, insect, animal, plant, and hybrid form. The common goal in information retrieval is to retrieve as many documents as possible from a collection that are closely related to an investigator's query. In this paper, we propose a model to support cyber forensics by utilizing an AvatarDrawn Ontological Knowledge Base (AOKB) in a document retrieval system. An advantage of this approach is that the AOKB can be progressively improved through definitions of new entities to expand its domain knowledge. An algorithm for semantic hand-drawn image retrieval is written to provide comprehensive and objective information.