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An Automated System for Criminal Police Reports Analysis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 942))

Abstract

Information Extraction (IE) and fusion are complex fields and have been useful in several domains to deal with heterogeneous data sources. Criminal police are challenged in forensics activities with the extraction, processing and interpretation of numerous documents from different types and with distinct formats (templates), such as narrative criminal reports, police databases and the result of OSINT activities, just to mention a few. Such challenges suggest, among others, to cope with and manually connect some hard to interpret meanings, such as license plates, addresses, names, slang and figures of speech. This paper aims to deal with forensic IE and fusion, thus a system was proposed to automatically extract, transform, clean, load and connect police reports that arrived from different sources. The same system aims to help police investigators to identify and correlate interesting extracted entities.

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Notes

  1. 1.

    Data collected from publicly available sources to be used in an intelligence context.

  2. 2.

    www.ansr.pt.

  3. 3.

    https://docs.oracle.com/javase/7/docs/technotes/guides/language/.

  4. 4.

    https://tika.apache.org/.

  5. 5.

    http://opennlp.sourceforge.net/models-1.5/.

  6. 6.

    http://www.linguateca.pt/floresta/ficheiros/gz/amazonia.ad.gz.

  7. 7.

    http://www.overmundo.com.br/.

  8. 8.

    https://opennlp.apache.org/.

  9. 9.

    https://gramatica.usc.es/pln/.

References

  1. Kakish, K., Kraft, T.A.: ETL evolution for real-time data warehousing. In: Proceedings of the Conference on Information Systems Applied Research ISSN, vol. 2167, pp. 1508 (2012)

    Google Scholar 

  2. Li, X.: Real-time data ETL framework for big real-time data analysis. In: IEEE International Conference on Information and Automation, no. August, pp. 1289–1294 (2015)

    Google Scholar 

  3. Majeed, F., Mahmood, M.S., Iqbal, M.: Efficient data streams processing in the real time data warehouse. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 5, pp. 57–61, July 2010

    Google Scholar 

  4. Song, J., Bao, Y., Shi, J.: A triggering and scheduling approach for ETL in a real-time data warehouse. In: 2010 10th IEEE International Conference on Computer and Information Technology, pp. 91–98, June 2010

    Google Scholar 

  5. Chávez, J.V., Li, X.: Ontology based ETL process for creation of ontological data warehouse. In: 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, pp. 1–6, October 2011

    Google Scholar 

  6. Jiang, L., Cai, H., Xu, B.: A domain ontology approach in the ETL process of data warehousing. In: 2010 IEEE 7th International Conference on E-Business Engineering, pp. 30–35, November 2010

    Google Scholar 

  7. Skoutas, D., Simitsis, A.: Ontology-based conceptual design of ETL processes for both structured and semi-structured data. Int. J. Semant. Web Inf. Syst. (IJSWIS) 3(4), 1–24 (2007)

    Article  Google Scholar 

  8. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  9. Dozier, C., Kondadadi, R., Light, M., Vachher, A., Veeramachaneni, S., Wudali, R.: Named entity recognition and resolution in legal text. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts, pp. 27–43. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Arulanandam, R., Savarimuthu, B.T.R., Purvis, M.A.: Extracting crime information from online newspaper articles. In: Proceedings of the Second Australasian Web Conference - Volume 155, AWC 2014, Darlinghurst, Australia, pp. 31–38. Australian Computer Society, Inc. (2014)

    Google Scholar 

  11. Shabat, H.A., Omar, N.: Named entity recognition in crime news documents using classifiers combination. Middle-East J. Sci. Res. 23(6), 1215–1221 (2015)

    Google Scholar 

  12. Yang, Y., Manoharan, M., Barber, K.S.: Modelling and analysis of identity threat behaviors through text mining of identity theft stories. In: 2014 IEEE Joint Intelligence and Security Informatics Conference, pp. 184–191, September 2014

    Google Scholar 

  13. Bsoul, Q., Salim, J., Zakaria, L.Q.: Effect verb extraction on crime traditional cluster. World Appl. Sci. J. 34(9), 1183–1189 (2016)

    Google Scholar 

  14. Schraagen, M.: Evaluation of Named Entity Recognition in Dutch online criminal complaints. Comput. Linguist. Neth. J. 7, 3–15 (2017)

    Google Scholar 

  15. Al-Zaidy, R., Fung, B.C.M., Youssef, A.M.: Towards discovering criminal communities from textual data. In: Proceedings of the 2011 ACM Symposium on Applied Computing, SAC 2011, pp. 172–177. ACM, New York (2011)

    Google Scholar 

  16. Sharnagat, R.: Named entity recognition: a literature survey. Center For Indian Language Technology (2014)

    Google Scholar 

  17. Bick, E.: The parsing system “PALAVRAS”: automatic grammatical analysis of Portuguese in a constraint grammar framework. 2000. 412 f. Ph.D. thesis, Aarhus University, Denmark University Press (2000)

    Google Scholar 

  18. Rodrigues R., Gomes, P.: Rapport–a Portuguese question-answering system. In: Portuguese Conference on Artificial Intelligence, pp. 771–782. Springer (2015)

    Google Scholar 

  19. Mansouri, A., Affendey, L.S., Mamat, A.: Named entity recognition approaches. J. Comput. Sci. 8(2), 339–344 (2008)

    Google Scholar 

  20. Konkol, I.M.: Named entity recognition. Ph.D. thesis, University of West Bohemia (2015)

    Google Scholar 

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Correspondence to Gonçalo Carnaz .

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Carnaz, G., Beires Nogueira, V., Antunes, M., Ferreira, N. (2020). An Automated System for Criminal Police Reports Analysis. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_36

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