Ph.D. Iacopo Vagliano

Knowledge Discovery
T: +49-431-8814-427
Düsternbrooker Weg 120
24105 Kiel

Function

  • Postdoctoral researcher

Key activities

  • Linked Data
  • Recommender System
  • Text Mining

Professional background

  • 2008-2011
    Bachelor student at Politecnico di Torino, Italy
  • 2011-2013
    Master student at Politecnico di Torino, Italy
  • März-August 2013
    Visiting student at INRIA Sophia Antipolis, France
  • 2014-2017
    Ph.D. student at Politecnico di Torino, Italy
  • September 2015 - Februar 2016
    Visiting Ph.D. student at Gdansk University of Technology, Poland
  • 2017
    Ph.D. in Computer and System Engineering with a thesis on "Content Recommendation through Linked Data"
  • since 2017
    Postdoctoral researcher in the Knowledge Discovery group at ZBW

Other professional activities

Collaboration in projects

  • Since 2017
    WP3 leader of the EU project MOVING
  • July 2015 - July 2016
    software developer for the EU projectCRYSTAL

Service to the Community

  • Reviewer for the International Journal on Semantic Web and Information Systems (IJSWIS), ad hoc
  • Reviewer for the 8th Italian Information Retrieval Workshop (IIR 2017)
  • Session Chair at the 7th Conference on the Internet of Things and Smart Spaces (ruSMART 2014)
  • Student volunteer at the Empirical Software Engineering International Week 2014

Teaching

  • • Teaching assistant at Politecnico di Torino in Software Engineering, Information Systems, and Object Oriented Programming
  • • Co-supervision of Master thesis together with Prof. Maurizio Morisio

Publications

Conference Papers (peer reviewed)

Vagliano, Iacopo / Monti, Diego / Morisio, Maurizio
SemRevRec: A Recommender System based on User Reviews and Linked Data
In: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 28, 2017, CEUR Workshop Proceedings, Aachen: RWTH, 2017, Link to fulltext (PDF)
Vagliano, Iacopo / Monti, Diego / Scherp, Ansgar / Morisio, Maurizio
Content Recommendation through Semantic Annotation of User Reviews and Linked Data
In: K-CAP 2017: Proceedings of the Knowledge Capture Conference, Article No. 32, Austin, TX, USA, December 04 - 06, 2017 New York, NY: ACM, 2017,, doi:10.1145/3148011.3148035 Link to fulltext (PDF)