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

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.

[1]  Raghu Ramakrishnan,et al.  Managing information extraction: state of the art and research directions , 2006, SIGMOD Conference.

[2]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[3]  K. Gurney,et al.  Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.

[4]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..

[5]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[6]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[7]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[8]  David Maxwell Chickering,et al.  Learning Bayesian Networks From Dependency Networks: A Preliminary Study , 2003, AISTATS.

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  Lise Getoor,et al.  Relationship Identification for Social Network Discovery , 2007, AAAI.

[11]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[12]  Roger Burrows,et al.  After the crisis? Big Data and the methodological challenges of empirical sociology , 2014 .

[13]  Richard Hill,et al.  A Kuramoto Model Based Approach to Extract and Assess Influence Relations , 2015, ISICA.

[14]  C. Fellbaum.  An Electronic Lexical Database , 1998 .

[15]  Yuni Xia,et al.  Bridging Text Mining and Bayesian Networks , 2009, 2009 International Conference on Network-Based Information Systems.

[16]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..

[17]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[18]  Nik Bessis,et al.  An influence assessment method based on co-occurrence for topologically reduced big data sets , 2016, Soft Comput..