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Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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dc.contributor.author Brown, Peter
dc.contributor.author Raote, Ishier
dc.contributor.author RELISH Consortium
dc.contributor.author Zhou, Yaoqi
dc.date.accessioned 2022-05-09T11:32:21Z
dc.date.available 2022-05-09T11:32:21Z
dc.date.issued 2019
dc.identifier.citation Brown P, RELISH Consortium, Raote I, Zhou Y. Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database (Oxford). 2019 Jan 1;2019:baz085. DOI:10.1093/database/baz085
dc.identifier.issn 1758-0463
dc.identifier.uri http://hdl.handle.net/10230/53028
dc.description.abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
dc.description.sponsorship Funding: Griffith University Gowonda HPC Cluster; Queensland Cyber Infrastructure Foundation
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Oxford University Press
dc.rights © Peter Brown et al. 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Medicina i literatura
dc.subject.other National Institutes of Health (U.S.) -- PubMed Central
dc.subject.other Cerca documental
dc.title Large expert-curated database for benchmarking document similarity detection in biomedical literature search
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1093/database/baz085
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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