article

Semantic representation and enrichment of information retrieval experimental data

Authors:

Gianmaria Silvello,

Georgeta Bordea,

Paul Buitelaar,

Toine BogersAuthors Info & Claims

International Journal on Digital Libraries, Volume 18, Issue 2

Pages 145 - 172

https://doi.org/10.1007/s00799-016-0172-8

Published: 01 June 2017 Publication History

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. In this work, we discuss how to semantically annotate and interlink this data, with the goal of enhancing their interpretation, sharing, and reuse. We discuss the underlying evaluation workflow and propose a resource description framework model for those workflow parts. We use expertise retrieval as a case study to demonstrate the benefits of our semantic representation approach. We employ this model as a means 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 topic-centric 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. Several methods for expert profiling and expert finding are analysed and evaluated. Our results show that it is possible to construct expert profiles starting from automatically extracted expertise topics and that topic-centric approaches outperform state-of-the-art language modelling approaches for expert finding.

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Index Terms

Semantic representation and enrichment of information retrieval experimental data
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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International Journal on Digital Libraries Volume 18, Issue 2

Jun 2017

96 pages

ISSN:1432-5012

EISSN:1432-1300

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Ferro N(2024)What Happened in CLEF For Another While?Experimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-031-71736-9_1(3-57)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71736-9_1
Ni T(2023)An Intelligent Retrieval Algorithm for Digital Literature Promotion Information Based on TRS Information RetrievalInternational Journal of Information Technologies and Systems Approach10.4018/ijitsa.31845816:2(1-14)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.4018/ijitsa.318458
Bukowski MGeisler SSchmitz-Rode TFarkas R(2020)Feasibility of activity-based expert profiling using text mining of scientific publications and patentsScientometrics10.1007/s11192-020-03414-8123:2(579-620)Online publication date: 18-Mar-2020
https://dl.acm.org/doi/10.1007/s11192-020-03414-8
Ferro N(2019)What Happened in CLEF For a While?Experimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-030-28577-7_1(3-45)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-28577-7_1
Ferro N(2017)Reproducibility Challenges in Information Retrieval EvaluationJournal of Data and Information Quality10.1145/30202068:2(1-4)Online publication date: 4-Jan-2017
https://dl.acm.org/doi/10.1145/3020206

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