Knowledge Management and Human Trafficking: Using Conceptual Knowledge Representation, Text Analytics and Open-Source Data to Combat Organized Crime

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)


Globalization, the ubiquity of mobile communications and the rise of the web have all expanded the environment in which organized criminal entities are conducting their illicit activities, and as a result the environment that law enforcement agencies have to police. This paper triangulates the capability of open-source data analytics, ontological knowledge representation and the wider notion of knowledge management (KM) in order to provide an effective, interdisciplinary means to combat such threats, thus providing law enforcement agencies (LEA’s) with a foundation of competitive advantage over human trafficking and other organized crime.


Knowledge Management Organize Crime Sentiment Analysis Human Trafficking Conceptual Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Elliott, A., Urry, J.: Mobile lives. Routledge (2010)Google Scholar
  2. 2.
    Krikorian, R.: Twitter Engineering Blog: New Tweets per Second Records, and How!
  3. 3.
    ePOOLICE. ePOOLICE - About,
  4. 4.
    Gottschalk, P.: Knowledge management technology for organized crime risk assessment. Inf. Syst. Front. 12(3), 267–275 (2010),
  5. 5.
    Rankin, G., Kinsella, N.: Human trafficking–The importance of knowledge information exchange. In: Intelligence Management, pp. 159–180. Springer (2011)Google Scholar
  6. 6.
    United Nations Office on Drugs and Crime (UNODC), United Nations Convention Against Transnational Organised Crime and the Protocols Thereto (2004)Google Scholar
  7. 7.
    Serious Organised Crime Agency (SOCA), UKHTC: A Strategic Assessment on the Nature and Scale of Human Trafficking in 2012 (2013)Google Scholar
  8. 8.
  9. 9.
    United Nations Office on Drugs and Crime (UNODC), Global Report on Trafficking in Persons (2012)Google Scholar
  10. 10.
    United Nations Office on Drugs and Crime (UNODC), Anti-Human Trafficking Manual for Criminal Justice Practitioners (2009)Google Scholar
  11. 11.
    Logan, T.K., Walker, R., Hunt, G.: Understanding human trafficking in the united states. Trauma. Violence Abuse 10(1), 3–30 (2009)CrossRefGoogle Scholar
  12. 12.
    Laczko, F.: Enhancing Data Collection and Research on Trafficking in Persons. In: Measuring Human Trafficking, pp. 37–44. Springer (2007)Google Scholar
  13. 13.
    Tyldum, G., Brunovskis, A.: Describing the Unobserved: Methodological Challenges in Empirical Studies on Human Trafficking. In: Laczko, F., Godziak, E. (eds.) Data and Research on Human Trafficking: A Global Survey. IOM International Organisation for Migration, Geneva (2005)Google Scholar
  14. 14.
    United Nations Office on Drugs and Crime (UNODC), Global Report on Trafficking in Persons (2009)Google Scholar
  15. 15.
    EUROPOL. EU Serious and Organised Crime Threat Assessment,
  16. 16.
    Human Trafficking Foundation. Modern Day Slavery in British Nail Bars,
  17. 17.
  18. 18.
    Siskin, A., Wyler, L.S.: Trafficking in persons: US policy and issues for congress. Springer (2012)Google Scholar
  19. 19.
    Arbuthnott, D.: Beauty and the beasts: Slaves in Britain. The Sunday Times Magazine (2013) (August 18, 2010)Google Scholar
  20. 20.
    Criminal Intelligence Service Canada (CISC). Strategic Early Warning for Criminal Intelligence,
  21. 21.
    Chaves, T.D., Pendleton, M.R., Bueerman, C.J.: Knowledge management in policing. Community Oriented Policing Services (COPS) US Department of Justice (2005)Google Scholar
  22. 22.
    Hughes, R.G., Stoddart, K.: Hope and fear: Intelligence and the future of global security a decade after 9/11. Intelligence and National Security 27(5), 625–652 (2012), CrossRefGoogle Scholar
  23. 23.
    Akhgar, B., Yates, S.: Intelligence Management: Knowledge Driven Frameworks for Combating Terrorism and Organized Crime. Springer (2011)Google Scholar
  24. 24.
    Wiig, K.M.: Knowledge management: An emerging discipline rooted in a long history. In: Knowledge Horizons: The Present and the Promise of Knowledge Management, pp. 3–26 (2000)Google Scholar
  25. 25.
    Dalkir, K.: Knowledge management in theory and practice. Routledge (2013)Google Scholar
  26. 26.
    United Nations Office on Drugs and Crime (UNODC). Voluntary Reporting System on Migrant Smuggling and Related Conduct (VRS-MSRC),
  27. 27.
    Cook, S.D., Brown, J.S.: Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science 10(4), 381–400 (1999)CrossRefGoogle Scholar
  28. 28.
    Nonaka, I., Toyama, R., Konno, N.: SECI, ba and leadership: A unified model of dynamic knowledge creation. Long Range Plann. 33(1), 5–34 (2000)CrossRefGoogle Scholar
  29. 29.
    Boisot, M.: Knowledge assets: Securing competitive advantage in the knowledge economy (1998)Google Scholar
  30. 30.
    Meyer, M., Zack, M.: The design and implementation of information products. Sloan Management Review 37(3), 43–59 (1996)Google Scholar
  31. 31.
    Wiig, K.: Knowledge management foundations. In: Thinking about Thinking. How People and Organizations Create, Represent, and Use Knowledge, Arlington, TX, USA (1993)Google Scholar
  32. 32.
    Nonaka, I., Takeuchi, H.: The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press, New York (1995)Google Scholar
  33. 33.
    Sowa, J.F.: The challenge of knowledge soup. In: Research Trends in Science, Technology and Mathematics Education, pp. 55–90 (2006)Google Scholar
  34. 34.
    Chen, H., Zeng, D., Atabakhsh, H., Wyzga, W., Schroeder, J.: COPLINK: Managing law enforcement data and knowledge. Communications of the ACM 46(1), 28–34 (2003)CrossRefGoogle Scholar
  35. 35.
  36. 36.
    Polovina, S.: An introduction to conceptual graphs. In: Priss, U., Polovina, S., Hill, R. (eds.) ICCS 2007. LNCS (LNAI), vol. 4604, pp. 1–14. Springer, Heidelberg (2007)Google Scholar
  37. 37.
    Sowa, J.F.: Conceptual structures: information processing in mind and machine. Addison-Wesley Longman Publishing Co., Inc. (1984)Google Scholar
  38. 38.
    ILO (International Labour Office), Operational Indicators of Traffcking in Human Beings (2009)Google Scholar
  39. 39.
    CoGui. CoGui in a Nutshell,
  40. 40.
    Feldman, R., Sanger, J.: The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press (2007)Google Scholar
  41. 41.
    Reamy, T.: Enterprise content categorization - how to sucessfully choose, develop and implement a semantic strategy. Knowledge Architecture Professional Services, KAPS Group (2010)Google Scholar
  42. 42.
    Chau, M., Xu, J.J., Chen, H.: Extracting meaningful entities from police narrative reports. In: Proceedings of the 2002 Annual National Conference on Digital Government Research, pp. 1–5 (2002)Google Scholar
  43. 43.
    Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime data mining: A general framework and some examples. Computer 37(4), 50–56 (2004)CrossRefGoogle Scholar
  44. 44.
    Reamy, T.: Enterprise content categorization - the business strategy for a semantic infrastructure. Knowledge Architecture Professional Services, KAPS Group (2010)Google Scholar
  45. 45.
    Hu, X., Liu, H.: Text analytics in social media. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 385–414. Springer (2012)Google Scholar
  46. 46.
    SAS Institute Inc. SAS Content Categorization Studio 12.1: User’s Guide,
  47. 47.
    MaxMind. Free World Cities Database,

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CENTRICSheffield Hallam UniversitySheffieldUK
  2. 2.Rankin Kinsella AssociatesBirkenheadUK

Personalised recommendations