An architecture for the autonomic curation of crowdsourced knowledge

Alina Patelli*, Peter R. Lewis, Anikó Ekárt, Hai Wang, Ian T. Nabney, David Bennett, Ralph Lucas, Alex Cole

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)2031–2046
Number of pages16
JournalCluster Computing
Issue number3
Early online date9 Jun 2017
Publication statusPublished - 1 Sept 2017

Bibliographical note

© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


  • autonomic computing
  • knowledge curation
  • ontologies
  • semantic technologies


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