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Semantic representation and enrichment of information retrieval experimental data

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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Notes

  1. http://www.europeana.eu/

  2. http://id.loc.gov/

  3. http://lod.springer.com/

  4. http://data.elsevier.com/

  5. http://data.nature.com/

  6. http://trec.nist.gov/

  7. http://www.clef-initiative.eu/

  8. http://research.nii.ac.jp/ntcir/

  9. https://lucene.apache.org/openrelevance/

  10. http://wice.csse.unimelb.edu.au:15000/evalweb/ireval/

  11. http://www.cs.waikato.ac.nz/ml/weka/

  12. Blank nodes do not have identifiers in the RDF abstract syntax. The blank node identifiers have local scope and are purely an artefact of the serialization. Refer to http://www.w3.org/TR/rdf11-concepts/#section-blank-nodes for more details on blank nodes.

  13. http://www.dbpedia.org/

  14. http://dblp.l3s.de/

  15. http://www.w3.org/TR/WD-rdf-syntax-971002/

  16. http://trec.nist.gov/trec_eval/

  17. http://www.w3.org/TR/turtle/

  18. http://www.w3.org/TeamSubmission/n3/

  19. http://www.w3.org/TR/void/

  20. Linked Data: http://linkeddata.org

  21. DBpedia: http://dbpedia.org/

  22. http://dblp.uni-trier.de/

  23. Available at http://arnetminer.org/DBLP_Citation, last accessed July 9, 2013.

  24. A preliminary test on just the publications from the core venues showed that adding quotes around the publication title decreased recall from 80.3 to 70.86 %.

  25. http://toinebogers.com/?page_id=660

  26. An approach based on a Semantic Web search engine that uses key phrase search to find structured data was also considered, restricting the search to the DBPedia domain. The results were disappointing, because only a limited number of retrieved results can be analysed. Often, the relevant DBpedia concept does not appear in the top results.

  27. http://www.freebase.com/

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Acknowledgments

This work has been funded in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT). The authors would like to thank Barry Coughlan for his suggestions and collaboration.

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Silvello, G., Bordea, G., Ferro, N. et al. Semantic representation and enrichment of information retrieval experimental data. Int J Digit Libr 18, 145–172 (2017). https://doi.org/10.1007/s00799-016-0172-8

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

Keywords

  • Experimental data
  • Expertise profiling
  • Expert search
  • Information retrieval evaluation
  • Resource description framework
  • Semantic enrichment