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Progress in Artificial Intelligence

, Volume 7, Issue 4, pp 251–272 | Cite as

SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

  • Yaroslav NechaevEmail author
  • Francesco Corcoglioniti
  • Claudio Giuliano
Regular Paper

Abstract

SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink.

Keywords

Social Media Linked Open Data Machine learning DBpedia 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fondazione Bruno KesslerTrentoItaly
  2. 2.University of TrentoTrentoItaly

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