Sennaike, Oladipupo A. and Waqar, Mohammad and Osagie, Edobor and Hassan, Islam and Stasieqicz, Arkadiusz and Porwol, Lukasz and Ojo, Adegboyega (2017) Towards intelligent open data platforms: Discovering relatedness in datasets. In: 2017 Intelligent Systems Conference (IntelliSys). IEEE, pp. 414-421. ISBN 9781509064359

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Abstract

Open data platforms are central to the management and exploitation of data ecosystems. While existing platforms provide basic search capabilities and features for filtering search results, none of the existing platforms provide recommendations on related datasets. Knowledge of dataset relatedness is critical for determining datasets that can be mashed-up or integrated for the purpose of analysis and creation of data-driven services. When considering data platforms, such as data.gov with over 193,000 datasets or data.gv.uk with over 40,000 datasets, specifying dataset relatedness relationship manually is infeasible. In this paper, we approach the problem of discovering relatedness in datasets by employing the Kohonen Self Organsing Map (SOM) algorithm to analyze the metadata extracted from the Data Catalogue maintained on a platform. Our results show that this approach is very effective in discovering relatedness relationships among datasets. Findings also reveal that our approach could uncover interesting and valuable connections among domains of the datasets which could be further exploited for designing smarter data-driven services.

Item Type:	Book Section
Additional Information:	Cite as: O. A. Sennaike et al., "Towards intelligent open data platforms: Discovering relatedness in datasets," 2017 Intelligent Systems Conference (IntelliSys), London, 2017, pp. 414-421, doi: 10.1109/IntelliSys.2017.8324327.
Keywords:	Semantic relatedness of datasets; data recommendation; open data platforms; e-government;
Academic Unit:	Faculty of Social Sciences > School of Business
Item ID:	13371
Identification Number:	https://doi.org/10.1109/IntelliSys.2017.8324327
Depositing User:	Adegboyega Ojo
Date Deposited:	02 Oct 2020 15:19
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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