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International Conference on Applications of Natural Language to Information Systems

NLDB 2016: Natural Language Processing and Information Systems pp 310–317Cite as

Mining Hidden Knowledge from the Counterterrorism Dataset Using Graph-Based Approach

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

Information overloaded is now a matter of fact. These enormous stack of information poses huge potential to discover previously uncharted knowledge. In this paper, we propose a graph based approach integrated with statistical correlation measure to discover latent but valuable information buried under huge corpora. For given two concepts, \(C_i\) and \(C_j\) (e.g. bush and bin ladin), we find the best set of intermediate concepts interlinking them by gleaning across multiple documents. We perform query enrichment on input concepts using Longest Common Substring (LCSubstr) algorithm to enhance the level of granularity. Moreover, we use Kulczynski correlation measure to determine the strength of interdependence between concepts and demote associations with relatively meager statistical significance. Finally, we present our users with ranked paths, along with sentence level evidence to facilitate better interpretation of underlying context. Counterterrorism dataset is used to demonstrate the effectiveness and applicability of our technique.

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Notes

  1. 1.

    https://www.nlm.nih.gov/pubs/factsheets/medline.html.

  2. 2.

    https://www.nlm.nih.gov/mesh.

  3. 3.

    http://www.opencalais.com/opencalais-demo/.

  4. 4.

    http://neo4j.com.

  5. 5.

    http://en.wikipedia.org/wiki/Longest_common_substring_problem.

  6. 6.

    http://govinfo.library.unt.edu/911/report/911Report.pdf.

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Acknowledgments

This work was supported by National Science Foundation grant IIS-1452898.

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Authors and Affiliations

  1. North Dakota State University, Fargo, ND, USA

    Kishlay Jha & Wei Jin

Authors
  1. Kishlay Jha
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  2. Wei Jin
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Corresponding author

Correspondence to Kishlay Jha .

Editor information

Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

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© 2016 Springer International Publishing Switzerland

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Jha, K., Jin, W. (2016). Mining Hidden Knowledge from the Counterterrorism Dataset Using Graph-Based Approach. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-41754-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41753-0

  • Online ISBN: 978-3-319-41754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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