Automated Extraction of Fragments of Bayesian Networks from Textual Sources

Marcello Trovati, Jer Hayes, Francesco Palmieri, Nik Bessis

Research output: Contribution to journalArticle (journal)peer-review

17 Citations (Scopus)
152 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)508-519
Number of pages12
JournalApplied Soft Computing
Early online date12 Jul 2017
Publication statusPublished - 1 Nov 2017


  • Text miningNetwork theoryBayesian networks
  • Text mining
  • Network theory
  • Bayesian networks


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