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

Information Sciences Institute, University of Southern California, Marina del Rey, CA 90502, USA
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Future Internet 2019, 11(3), 59; https://doi.org/10.3390/fi11030059
Received: 19 January 2019 / Revised: 20 February 2019 / Accepted: 22 February 2019 / Published: 4 March 2019
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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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. View Full-Text
Keywords: knowledge discovery; domain specific; no programming; knowledge graphs; information extraction; investigative domains; search; personalized analytics knowledge discovery; domain specific; no programming; knowledge graphs; information extraction; investigative domains; search; personalized analytics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kejriwal, M.; Szekely, P. myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming. Future Internet 2019, 11, 59.

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