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Text Mining of Related Events from Natural Science Literature

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Semantics, Analytics, Visualization. Enhancing Scholarly Data (SAVE-SD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9792))

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Abstract

We present an approach to text mining in areas where the entities of interest can not be defined in advance. Our system is aimed at finding related events in natural science literature, in particular, changing/increasing/decreasing variables in Marine science publications. It enables semantic search for events by abstracting from morphological, lexical-semantic and syntactic variations. In addition, generalisation of variables through syntactic pruning helps finding similar variables. Relations between events are induced from co-occurrence frequencies. Extracted information is stored in a property graph database and accessed using the Cypher query language. A user interface presents events as a graph to visualise their type, frequency and relation strength, in combination with their textual sources.

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Notes

  1. 1.

    http://www.nature.com/developers/documentation/api-references/opensearch-api.

  2. 2.

    http://neo4j.com/.

  3. 3.

    Graph rendering with vis.js Javascript library: http://visjs.org/.

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Acknowledgements

Financial aid from the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773) is gratefully acknowledged. We thank Murat Van Ardelan for sharing his knowledge of Marine science.

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Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Erwin Marsi & Pinar Øzturk

Authors
  1. Erwin Marsi
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  2. Pinar Øzturk
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Corresponding author

Correspondence to Erwin Marsi .

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Editors and Affiliations

  1. Oxford e-Research Centre, University of Oxford, Oxford, United Kingdom

    Alejandra González-Beltrán

  2. Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

    Francesco Osborne

  3. Dept of Computer Sci & Engineering, University of Bologna, Bologna, Italy

    Silvio Peroni

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Marsi, E., Øzturk, P. (2016). Text Mining of Related Events from Natural Science Literature. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds) Semantics, Analytics, Visualization. Enhancing Scholarly Data. SAVE-SD 2016. Lecture Notes in Computer Science(), vol 9792. Springer, Cham. https://doi.org/10.1007/978-3-319-53637-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-53637-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53636-1

  • Online ISBN: 978-3-319-53637-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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