TY - JOUR
T1 - Automated Extraction of Fragments of Bayesian Networks from Textual Sources
AU - Trovati, Marcello
AU - Hayes, Jer
AU - Palmieri, Francesco
AU - Bessis, Nik
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by big data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered.
In this article we propose and analyse a novel method to extract and build fragments of Bayesian Networks (BNs) from unstructured large data sources.
The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources.
The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.
AB - Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by big data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered.
In this article we propose and analyse a novel method to extract and build fragments of Bayesian Networks (BNs) from unstructured large data sources.
The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources.
The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios.
KW - Text miningNetwork theoryBayesian networks
KW - Text mining
KW - Network theory
KW - Bayesian networks
UR - http://www.scopus.com/inward/record.url?scp=85026241045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026241045&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/99153000-f84b-380b-8ce8-39211dd77ebd/
U2 - https://doi.org/10.1016/j.asoc.2017.07.009
DO - https://doi.org/10.1016/j.asoc.2017.07.009
M3 - Article (journal)
SN - 1568-4946
VL - 60
SP - 508
EP - 519
JO - Applied Soft Computing
JF - Applied Soft Computing
ER -