Abstract
A new test document collection, PMSC-UGR, is presented in this paper. It has been built using a large subset of MEDLINE/PubMed scientific articles, which have been subjected to a disambiguation process to identify unequivocally who are their authors (using ORCID). The collection has also been completed by adding citations to these articles available through Scopus/Elsevier’s API. Although this test collection can be used for different purposes, we focus here on its use for expert recommendation and document filtering, reporting some preliminary experiments and their results.
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Notes
- 1.
Profile extracted, for example, from documents authored by this individual.
- 2.
Although PubMed does not contain complete articles but references to articles, called citations, we will use the term articles to refer to these citations, and reserve the name citations to refer to other articles that cite in their bibliographic references a given article.
- 3.
- 4.
- 5.
In fact 26,661,157 articles in PubMed have not any ORCID.
- 6.
The data was downloaded from Scopus API between July 3 and September 27, 2017 via http://api.elsevier.com and http://www.scopus.com.
- 7.
The reason is that for these authors we cannot obtain citations to their articles, so this secondary collection is larger but contains less information.
- 8.
- 9.
We do not require a perfect match, allowing an edit distance of 5 for title and 3 for author.
- 10.
This may happen, for example, when the articles (probably only one) in PubMed of an author (having ORCID and ScopusID) do not appear in the list of papers in Scopus written by this author.
- 11.
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Acknowledgment
This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under project TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER).
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Albusac, C., de Campos, L.M., Fernández-Luna, J.M., Huete, J.F. (2018). PMSC-UGR: A Test Collection for Expert Recommendation Based on PubMed and Scopus. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_4
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