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Ontology Population from Raw Text Corpus for Open-Source Intelligence

  • Giulio Ganino
  • Domenico LemboEmail author
  • Federico Scafoglieri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10544)

Abstract

Open-Source INTelligence (OSINT) is intelligence based on publicly available sources, such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled from it, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but due to the vast amount of documents available, automatic mechanisms for their population starting from the crawled text are needed. In this paper, we present an approach for the automatic population of pre-defined ontologies based on the General Architecture for Text Engineering (GATE) system. We present some experimental results, which are encouraging in terms of extracted correct instances of the ontology. Finally, we describe an alternative approach and additional experiments for one of the phases of our pipeline, which requires the use of pre-defined dictionaries for relevant entities. Thanks to such variant, we were able to reduce the manual effort required in this phase, still obtaining promising results.

Notes

Acknowledgments

This work has been partly supported by Leonardo Company (formerly Selex ES) in the context of the XASMOS initiative, and by the Italian project RoMA (SCN_00064). The work of Giulio Ganino has been supported by the FILAS grant Laboratori teorico-sperimentali a supporto delle applicazioni spaziali delle industrie laziali (FILAS-RU-2014-1058).

References

  1. 1.
    Antonioli, N., Castanò, F., Coletta, S., Grossi, S., Lembo, D., Lenzerini, M., Poggi, A., Virardi, E., Castracane, P.: Ontology-based data management for the Italian public debt. Proceedings of FOIS 2014, 372–385 (2014)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, New York (2007)zbMATHGoogle Scholar
  3. 3.
    Baldoni, R., Nicola, R.D.: The White Book on Cyber-security (2015)Google Scholar
  4. 4.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly, Beijing (2009)zbMATHGoogle Scholar
  5. 5.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)zbMATHGoogle Scholar
  6. 6.
    Cunningham, H.: Developing Language Processing Components with GATE Version 8. University of Sheffield Department of Computer Science (2014)Google Scholar
  7. 7.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A framework and graphical development environment for robust NLP tools and applications. In: Proceedings of ACL 2002 (2002)Google Scholar
  8. 8.
    Guarino, N.: Formal ontology in information systems. In: Proceedings of FOIS 1998, Frontiers in Artificial Intelligence, pp. 3–15. IOS Press (1998)Google Scholar
  9. 9.
    Johnson, M., Khudanpur, S., Ostendorf, M., Rosenfeld, R.: Mathematical Foundations of Speech and Language Processing. Springer, New York (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kibble, R.: Introduction to Natural Language Processing. University of London (2013)Google Scholar
  11. 11.
    Maynard, D., Li, Y., Peters, W.: NLP techniques for term extraction and ontology population. In: Ontology Learning and Population: Bridging the Gap between Text and Knowledge, pp. 107–127. IOS Press (2008)Google Scholar
  12. 12.
    Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), 1–69 (2009)CrossRefGoogle Scholar
  13. 13.
    Scannapieco, M., Barcaroli, G., Summa, D., Scarnò, M.: Using internet as a data source for official statistics: a comparative analysis of web scraping technologies. In: Proceedings of NTTS 2015 (2015)Google Scholar
  14. 14.
    Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, pp. 44–49 (1994)Google Scholar
  15. 15.
    Witte, R., Khamis, N., Rilling, J.: Flexible ontology population from text: the OwlExporter. In: Proceedings of LREC 2010. May 2010Google Scholar
  16. 16.
    Zhao, H., Zhang, X., Kit, C.: Integrative semantic dependency parsing via efficient large-scale feature selection. J Artif. Intell. Res. 46, 203–233 (2013)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Giulio Ganino
    • 1
  • Domenico Lembo
    • 1
    Email author
  • Federico Scafoglieri
    • 1
  1. 1.Sapienza Università di Roma, DIAGRomeItaly

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