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Advances onto the Internet of Things pp 105–119Cite as

A Structural Approach to Infer Recurrent Relations in Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 260))

Abstract

Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word “knowledge” refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge embedded in data, we have built a system able to pick up some strategies in the solution of complex puzzle game.

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Acknowledgments

This work is partially supported by the PO FESR 2007/2013 grant G73F11000130004 funding the SmartBuildings project and by the PON R&C grant MI01_00091 funding the SeNSori project

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Authors and Affiliations

  1. DICGIM, Viale delle Scienze, University of Palermo, Edificio 6, 90128, Palermo, Italy

    Pietro Cottone, Salvatore Gaglio & Marco Ortolani

Authors
  1. Pietro Cottone
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  2. Salvatore Gaglio
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  3. Marco Ortolani
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Corresponding author

Correspondence to Marco Ortolani .

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Editors and Affiliations

  1. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Università di Palermo, Palermo, Italy

    Salvatore Gaglio

  2. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Università di Palermo, Palermo, Italy

    Giuseppe Lo Re

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Cottone, P., Gaglio, S., Ortolani, M. (2014). A Structural Approach to Infer Recurrent Relations in Data. In: Gaglio, S., Lo Re, G. (eds) Advances onto the Internet of Things. Advances in Intelligent Systems and Computing, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-03992-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-03992-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03991-6

  • Online ISBN: 978-3-319-03992-3

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