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.
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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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Behnaz, A., Bandara, M., Rabhi, F.A., Peat, M. (2019). A Statistical Learning Ontology for Managing Analytics Knowledge. In: Mehandjiev, N., Saadouni, B. (eds) Enterprise Applications, Markets and Services in the Finance Industry. FinanceCom 2018. Lecture Notes in Business Information Processing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-19037-8_12
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