Automatically curating online available knowledge is a pressing necessity, given the exponential increase in the volume of data published over the web. However, the solutions presently available are yet to reach the same level of support quality provided by human curators. This is mainly due to the fact that digital database managers do not take the expertise of the interested community into account nor exploit the underlying connections between knowledge pieces when processing user queries. We propose an approach to bridge the gap between automated curation and the one provided by human experts and implement it in the field of career data management. The resulting platform, Aviator, is based on an ontology powered autonomic manager capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. We provide numeric and use case based evidence to support these research claims.
Patelli, A., Lewis, P. R., Wang, H., Nabney, I., Bennett, D., Lucas, R., & Coles, A. (2016). Autonomic curation of crowdsourced knowledge: the case of career data management. In Proceedings : 2016 International Conference on Cloud and Autonomic Computing (pp. 40-49). IEEE Computer Society. https://doi.org/10.1109/ICCAC.2016.20