Recommending tripleset interlinking through a social network approach

Tripleset interlinking is one of the main principles of Linked Data. However, the discovery of existing triplesets relevant to be linked with a new tripleset is a non-trivial task in the publishing process. Without prior knowledge about the entire Web of Data, a data publisher must perform an exploratory search, which demands substantial effort and may become impracticable, with the growth and dissemination of Linked Data. Aiming at alleviating this problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90% the number of triplesets to be further inspected for establishing appropriate links. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41230-1_13.CNPq/160326/2012-5CNPq/301497/2006-0CNPq/475717/2011-2CNPq/57128/2009-9FAPERJ/E-26/170028/2008FAPERJ/E-26/103.070/2011CAPES/PROCAD/NF 1128/201

Recommending Tripleset Interlinking through a Social Network Approach

Abstract. Tripleset interlinking is one of the main principles of Linked Data. However, the discovery of existing triplesets relevant to be linked with a new tripleset is a non-trivial task in the publishing process. With-out prior knowledge about the entire Web of Data, a data publisher must perform an exploratory search, which demands substantial effort and may become impracticable, with the growth and dissemination of Linked Data. Aiming at alleviating this problem, this paper proposes a recommendation approach for this scenario, using a Social Network perspective. The experimental results show that the proposed approach obtains high levels of recall and reduces in up to 90 % the number of triplesets to be further inspected for establishing appropriate links