International Semantic Web Conference

The Semantic Web - ISWC 2015 pp 205-221 | Cite as

Building and Using a Knowledge Graph to Combat Human Trafficking

  • Pedro Szekely
  • Craig A. Knoblock
  • Jason Slepicka
  • Andrew Philpot
  • Amandeep Singh
  • Chengye Yin
  • Dipsy Kapoor
  • Prem Natarajan
  • Daniel Marcu
  • Kevin Knight
  • David Stallard
  • Subessware S. Karunamoorthy
  • Rajagopal Bojanapalli
  • Steven Minton
  • Brian Amanatullah
  • Todd Hughes
  • Mike Tamayo
  • David Flynt
  • Rachel Artiss
  • Shih-Fu Chang
  • Tao Chen
  • Gerald Hiebel
  • Lidia Ferreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9367)

Abstract

There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this paper we present an approach to building knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources, to scale to billions of triples extracted from the crawled content, and to support interactive queries on the data. We applied our approach, implemented in the DIG system, to the problem of combating human trafficking and deployed it to six law enforcement agencies and several non-governmental organizations to assist them with finding traffickers and helping victims.

Keywords

Linked data Knowledge graphs Entity linkage Data integration Information extraction 

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References

  1. 1.
    Benjelloun, O., Garcia-Molina, H., Menestrina, D., Su, Q., Whang, S.E., Widom, J.: Swoosh: A generic approach to entity resolution. The VLDB Journal 18(1), 255–276 (2009). http://dx.doi.org/10.1007/s00778-008-0098-x CrossRefGoogle Scholar
  2. 2.
    Bizer, C., Schultz, A.: The r2r framework: publishing and discovering mappings on the web. In: Workshop on Consuming Open Linked Data (COLD) (2010)Google Scholar
  3. 3.
    Chen, T., Borth, D., Darrell, T., Chang, S.: Deepsentibank: visual sentiment concept classification with deep convolutional neural networks. CoRR abs/1410.8586 (2014). http://arxiv.org/abs/1410.8586
  4. 4.
    Jentzsch, A., Isele, R., Bizer, C.: Silk: generating RDF links while publishing or consuming linked data. In: 9th International Semantic Web Conference (2010)Google Scholar
  5. 5.
    Knoblock, C.A., Szekely, P., Ambite, J.L., Goel, A., Gupta, S., Lerman, K., Muslea, M., Taheriyan, M., Mallick, P.: Semi-automatically mapping structured sources into the semantic web. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 375–390. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  6. 6.
    Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML) (2001)Google Scholar
  7. 7.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of massive datasets. Cambridge University Press (2014)Google Scholar
  8. 8.
    Ramnandan, S.K., Mittal, A., Knoblock, C.A., Szekely, P.: Assigning semantic labels to data sources. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 403–417. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  9. 9.
    Schultz, A., Matteini, A., Isele, R., Mendes, P.N., Bizer, C., Becker, C.: LDIF: A framework for large-scale linked data integration. In: 21st International World Wide Web Conference (WWW 2012). Developers Track (2012)Google Scholar
  10. 10.
    Taheriyan, M., Knoblock, C.A., Szekely, P., Ambite, J.L.: A graph-based approach to learn semantic descriptions of data sources. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 607–623. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  11. 11.
    Taheriyan, M., Knoblock, C.A., Szekely, P., Ambite, J.L.: A scalable approach to learn semantic models of structured sources. In: Proceedings of the 8th IEEE International Conference on Semantic Computing (ICSC 2014) (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pedro Szekely
    • 1
  • Craig A. Knoblock
    • 1
  • Jason Slepicka
    • 1
  • Andrew Philpot
    • 1
  • Amandeep Singh
    • 1
  • Chengye Yin
    • 1
  • Dipsy Kapoor
    • 1
  • Prem Natarajan
    • 1
  • Daniel Marcu
    • 1
  • Kevin Knight
    • 1
  • David Stallard
    • 1
  • Subessware S. Karunamoorthy
    • 1
  • Rajagopal Bojanapalli
    • 1
  • Steven Minton
    • 2
  • Brian Amanatullah
    • 2
  • Todd Hughes
    • 3
  • Mike Tamayo
    • 3
  • David Flynt
    • 3
  • Rachel Artiss
    • 3
  • Shih-Fu Chang
    • 4
  • Tao Chen
    • 4
  • Gerald Hiebel
    • 5
  • Lidia Ferreira
    • 6
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina Del ReyUSA
  2. 2.InferLink CorporationEl SegundoUSA
  3. 3.Next Century CorporationColumbiaUSA
  4. 4.Columbia UniversityNew YorkUSA
  5. 5.Universitt InnsbruckInnsbruckAustria
  6. 6.Universidade Federal de Minas GeraisBelo HorizonteBrazil

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