myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming

Mayank Kejriwal and Pedro Szekely
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Mayank Kejriwal: Information Sciences Institute, University of Southern California, Marina del Rey, CA 90502, USA
Pedro Szekely: Information Sciences Institute, University of Southern California, Marina del Rey, CA 90502, USA

Future Internet, 2019, vol. 11, issue 3, 1-23

Abstract: With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an important problem. We describe myDIG, a highly modular, open source pipeline-construction system that is specifically geared towards investigative users (e.g., law enforcement) with no programming abilities. The myDIG system allows users both to build a knowledge graph of entities, relationships, and attributes for illicit domains from a raw HTML corpus and also to set up a personalized search interface for analyzing the structured knowledge. We use qualitative and quantitative data from five case studies involving investigative experts from illicit domains such as securities fraud and illegal firearms sales to illustrate the potential of myDIG.

Keywords: knowledge discovery; domain specific; no programming; knowledge graphs; information extraction; investigative domains; search; personalized analytics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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