IR Scientific data: How to semantically represent and enrich them

Toine Bogers, Georgeta Bordea, Paul Buitelaar, Nicola Ferro, Gianmaria Silvello

Research output: Journal Article or Conference Article in JournalConference articleResearchpeer-review

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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume1749
ISSN1613-0073
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event3rd Italian Conference on Computational Linguistics, CLiC-it 2016 and 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, EVALITA 2016 - Napoli, Italy
Duration: 5 Dec 20167 Dec 2016

Conference

Conference3rd Italian Conference on Computational Linguistics, CLiC-it 2016 and 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, EVALITA 2016
Country/TerritoryItaly
CityNapoli
Period05/12/201607/12/2016

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