Dependency networks extractions from textual sources - A case study in criminology

Marcello Trovati*

*Corresponding author for this work

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

Abstract

The identification and assessment of data is one of the crucial challenges within any scientific discipline. Due to the size, complexity of data, and its internal contradictory information, there are several challenges, which need to be fully addressed to ensure the advance of Data Science and its applications. This work focuses on an automatic approach to extract, identify and discover knowledge focusing on the creation on Dependency Networks (DN). These are powerful modelling tools to navigate throughout data and allow to determine how concepts influence one another. The main motivation of this work is to propose a method to facilitate the decision making and knowledge discovery process. The validation of the proposed approach will demonstrate the potential of this work, specifically focussing on Criminology.

Original languageEnglish
Title of host publication2017 Intelligent Systems Conference, IntelliSys 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages373-381
Number of pages9
ISBN (Electronic)9781509064359
DOIs
Publication statusPublished - 23 Mar 2018
Event2017 Intelligent Systems Conference, IntelliSys 2017 - London, United Kingdom
Duration: 7 Sep 20178 Sep 2017

Publication series

Name2017 Intelligent Systems Conference, IntelliSys 2017
Volume2018-January

Conference

Conference2017 Intelligent Systems Conference, IntelliSys 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/09/178/09/17

Keywords

  • criminology
  • dependency networks
  • Network theory
  • text mining

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