Autonomic curation of crowdsourced knowledge: the case of career data management

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Abstract

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.

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Publication date2016
Publication titleProceedings : 2016 International Conference on Cloud and Autonomic Computing : co-located with the Tenth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2016), ICCAC 2016
PublisherIEEE
Pages40-49
Number of pages10
ISBN (Electronic)978-1-5090-3536-6
Original languageEnglish
Event2016 International Conference on Cloud and Autonomic Computing - Augsburg, Germany

Conference

Conference2016 International Conference on Cloud and Autonomic Computing
Abbreviated titleICCAC 2016
CountryGermany
CityAugsburg
Period12/09/1616/09/16

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Employable Graduates; Exploitable Research

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