Dependency Networks Extractions from Textual Sources in Criminology: An Initial Implementation

Marcello Trovati, Philip Hodgson, Charlotte Hargreaves, Andrew David Baker, Lu Liu, Nik Bessis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

1 Citation (Scopus)

Abstract

The acquisition and understanding of data is of paramount importance in any scientific context. However, the complexity of data due to its exponentially increasing size, its dynamical properties, and its internal contradictory information, raises huge challenges, which are at the core of Big Data science. In this paper, we discuss an automatic method to identify, rank and discover knowledge specifically focusing on Criminology research. Our main motivation is to create a set of tools to guide criminology experts through the decision process and knowledge discovery. In our validation we will show the clear potential of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages276-282
Number of pages7
ISBN (Electronic)9781509022519
DOIs
Publication statusPublished - 19 May 2016
Event2nd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016 - Oxford, United Kingdom
Duration: 29 Mar 20161 Apr 2016

Publication series

NameProceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016

Conference

Conference2nd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016
Country/TerritoryUnited Kingdom
CityOxford
Period29/03/161/04/16

Keywords

  • Big Data
  • Criminology
  • Network Theory
  • Text Mining

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