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.
Original language | English |
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Title of host publication | Proceedings : 2016 International Conference on Cloud and Autonomic Computing |
Subtitle of host publication | co-located with the Tenth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2016), ICCAC 2016 |
Publisher | IEEE |
Pages | 40-49 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-5090-3536-6 |
DOIs | |
Publication status | Published - 8 Dec 2016 |
Event | 2016 International Conference on Cloud and Autonomic Computing: Tenth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2016) - Augsburg, Germany Duration: 12 Sept 2016 → 16 Sept 2016 |
Conference
Conference | 2016 International Conference on Cloud and Autonomic Computing |
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Abbreviated title | ICCAC 2016 |
Country/Territory | Germany |
City | Augsburg |
Period | 12/09/16 → 16/09/16 |