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A Statistical Learning Ontology for Managing Analytics Knowledge

  • Ali BehnazEmail author
  • Madhushi Bandara
  • Fethi A. Rabhi
  • Maurice Peat
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 345)

Abstract

This paper focuses on the use of knowledge management techniques to help organisations tap into the power of statistical learning when conducting analytics. Its main contribution is in the use of an ontology development process to derive the essential concepts required for an ontology to represent variables of interest and their interrelationships with each other and with statistical datasets. This ontology is developed with the help of two case studies in the area of digital marketing and commodity pricing. A number of competency questions have been designed to map to user requirements in both case studies. A prototype system has been developed using a semantic modelling tool and a semantic data repository to demonstrate that the proposed ontology can support the competency questions via semantic queries.

Keywords

Statistical learning Data science Computational social scientist Ontology Semantic technology 

Notes

Acknowledgements

We are grateful to Capsifi and Ignition Wealth, especially Terry Roach, Mark Fordree and Mike Giles for sponsoring the research which led to this paper. We are also grateful to Adnene Guabtni and Chedia Dhaoui for helping with digital marketing case study. We thank Gino Conte on the visualization development of the prototype application.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ali Behnaz
    • 1
    Email author
  • Madhushi Bandara
    • 1
  • Fethi A. Rabhi
    • 1
  • Maurice Peat
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.The University of Sydney Business SchoolSydneyAustralia

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