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Knowledge spring process: Towards discovering and reusing knowledge within linked open data foundations

  • Universidad de Matanzas Camilo Cienfuegos
  • University of Alicante

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge could be of greater value if it is available for later consumption and reusing. In this paper, we present the first version of the Knowledge Spring Process, an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on open data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is the concept of reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our Knowledge Spring Process is based on a model-driven viewpoint in order to easier deal with the wide diversity of open data formats.

Original languageEnglish
Title of host publicationDATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications
EditorsMarkus Helfert, Andreas Holzinger, Orlando Belo, Chiara Francalanci
PublisherSciTePress
Pages291-296
Number of pages6
ISBN (Electronic)9789897580352
DOIs
StatePublished - 2014
Externally publishedYes
Event3rd International Conference on Data Management Technologies and Applications, DATA 2014 - Vienna, Austria
Duration: 29 Aug 201431 Aug 2014

Publication series

NameDATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications

Conference

Conference3rd International Conference on Data Management Technologies and Applications, DATA 2014
Country/TerritoryAustria
CityVienna
Period29/08/1431/08/14

Keywords

  • Data Mining
  • Knowledge Discovery
  • Linked Open Data
  • Metamodeling
  • Model Driven Development

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