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Explaining Scientific and Technical Emergence Forecasting

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Applications of Social Media and Social Network Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

In decision support systems such as those designed to predict scientific and technical emergence based on analysis of collections of data the presentation of provenance lineage records in the form of a human-readable explanation has been shown to be an effective strategy for assisting users in the interpretation of results. This work focuses on the development of a novel infrastructure for enabling the explanation of hybrid intelligence systems including probabilistic models—in the form of Bayes nets—and the presentation of corresponding evidence. Our design leverages Semantic Web technologies—including a family of ontologies—for representing and explaining emergence forecasting for entity prominence. Our infrastructure design has been driven by two goals: first, to provide technology to support transparency into indicator-based forecasting systems; second, to provide analyst users context-aware mechanisms to drill down into evidence underlying presented indicators. The driving use case for our explanation infrastructure has been a specific analysis system designed to automate the forecasting of trends in science and technology based on collections of published patents and scientific journal articles.

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Notes

  1. 1.

    ARBITER: Abductive Reasoning Based on Indicators and Topics of EmeRgence.

  2. 2.

    Mapekus Publication ontology: http://mapekus.fiit.stuba.sk/?page=ontologies.

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Acknowledgments

Support has been provided by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20154. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government.

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, USA

    James R. Michaelis, Deborah L. McGuinness, Cynthia Chang & John Erickson

  2. BAE Systems, 6 New England Executive Park, Burlington, MA, 01803, USA

    Daniel Hunter & Olga Babko-Malaya

Authors
  1. James R. Michaelis
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  2. Deborah L. McGuinness
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  3. Cynthia Chang
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  4. John Erickson
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  5. Daniel Hunter
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  6. Olga Babko-Malaya
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Corresponding author

Correspondence to James R. Michaelis .

Editor information

Editors and Affiliations

  1. Department of Computational Intelligence, Wroclaw University of Technology, Wroclaw, Poland

    Przemysław Kazienko

  2. Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, Indiana, USA

    Nitesh Chawla

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Michaelis, J.R., McGuinness, D.L., Chang, C., Erickson, J., Hunter, D., Babko-Malaya, O. (2015). Explaining Scientific and Technical Emergence Forecasting. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-19003-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19002-0

  • Online ISBN: 978-3-319-19003-7

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