IR Scientific data: How to semantically represent and enrich them
- ,
- Georgeta Bordea,
- Paul Buitelaar,
- Nicola Ferro,
- Gianmaria Silvello
- Aalborg University,
- National University of Ireland,
- University of Padua,
- University of Padova
Publication Information
Output type
Original language
EnglishJournal (Volume, Issue Number)
CEUR Workshop Proceedings (Volume 1749)Publication milestones
- Published - 01/01/2016
Publication status
ISSN
1613-0073Publication IDs
- Scopus: 85009259773
Abstract
Experimental evaluation carried out in international large-scale campaigns is a fundamental pillar of the scientific and technological advancement of Information Retrieval (IR) systems. Such evaluation activities produce a large quantity of scientific and experimental data, which are the foundation for all the subsequent scientific production and development of new systems. We discuss how to annotate and interlink this data, by proposing a method for exposing experimental data as Linked Open Data (LOD) on the Web and as a basis for enriching and automatically connecting this data with expertise topics and expert profiles. In this context, a topiccentric approach for expert search is proposed, addressing the extraction of expertise topics, their semantic grounding with the LOD cloud, and their connection to IR experimental data.