Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to providing numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality.

Automatically curating knowledge that is available online 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 which produces 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.