TY - GEN
T1 - Dependency networks extractions from textual sources - A case study in criminology
AU - Trovati, Marcello
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/23
Y1 - 2018/3/23
N2 - 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.
AB - 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.
KW - criminology
KW - dependency networks
KW - Network theory
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85050884621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050884621&partnerID=8YFLogxK
U2 - 10.1109/IntelliSys.2017.8324321
DO - 10.1109/IntelliSys.2017.8324321
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85050884621
T3 - 2017 Intelligent Systems Conference, IntelliSys 2017
SP - 373
EP - 381
BT - 2017 Intelligent Systems Conference, IntelliSys 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Intelligent Systems Conference, IntelliSys 2017
Y2 - 7 September 2017 through 8 September 2017
ER -