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A Kuramoto Model Based Approach to Extract and Assess Influence Relations

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

Abstract

In this paper, we introduce a novel method to extract and assess influence relations between concepts, based on a variation of the Kuramoto Model. The initial evaluation focusing on an unstructured dataset provided by the abstracts and articles freely available from PubMed [7], shows the potential of our approach, as well as suggesting its applicability to a wide selection of multidisciplinary topics.

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References

  1. Trovati, M., Bessis, N., Huber, A., Zelenkauskaite, A., Asimakopoulou, E.: Extraction, Identification and Ranking of Network Structures from Data Sets. In: Proceedings of CISIS, pp. 331–337 (2014)

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  2. Trovati, M.: Reduced topologically real-world networks: a big-data approach. Int. J. Distrib. Syst. Technol. 6(2), 13–27 (2015)

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  3. Trovati, M.: An influence assessment method based on co-occurrence for topologically reduced big datasets. Soft Comput. 1–10 (2015). Springer, Berlin, Heidelberg

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  4. Trovati, M., Bessis, N., Palmieri, F., Hill, R.: Dynamical extraction and assessment of probabilistic information between concepts from unstructured large data sets. Submitted to IEEE transactions (2015)

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  5. Kuramoto, Y.: International Symposium on Mathematical Problems in Theoretical Physics. Lecture Notes in Physics, vol. 39. Springer, New York (1975)

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  6. Trovati, J., Trovati, P., Larcombe, L., Liu, A.: Semi-automated assessment of the direction of influence relations from semantic networks: a case study in maths anxiety. In: The Proceedings of IBDS (2015)

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  7. PubMed Website. http://www.ncbi.nlm.nih.gov/pubmed. Accessed September 2015

  8. Natural Language Toolkit Website. http://www.nltk.org/. Accessed September 2015

  9. Miller, G.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

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Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, University of Derby, Derby, UK

    Marcello Trovati, Nik Bessis & Richard Hill

  2. Dipartimento di Informatica, Universita’ di Salerno, Salerno, Italy

    Aniello Castiglione

  3. Department of Computing, Edgehill University, Ormskirk, UK

    Nik Bessis

Authors
  1. Marcello Trovati
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  2. Aniello Castiglione
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  3. Nik Bessis
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  4. Richard Hill
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Corresponding author

Correspondence to Marcello Trovati .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Trovati, M., Castiglione, A., Bessis, N., Hill, R. (2016). A Kuramoto Model Based Approach to Extract and Assess Influence Relations. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_49

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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