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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mayank Kejriwal & Pedro Szekely, 2019. "myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming," Future Internet, MDPI, vol. 11(3), pages 1-23, March.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:3:p:59-:d:210708
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    References listed on IDEAS

    as
    1. Elbashir, Mohamed Z. & Collier, Philip A. & Davern, Michael J., 2008. "Measuring the effects of business intelligence systems: The relationship between business process and organizational performance," International Journal of Accounting Information Systems, Elsevier, vol. 9(3), pages 135-153.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. Feng Niu & Ce Zhang & Christopher Ré & Jude Shavlik, 2012. "Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 8(3), pages 42-73, July.
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