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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
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.
Brown, Peter
e21132aa-29de-4c0e-846a-f090ddda0dbc
Zhou, Yaoqi
be98d1b9-cc81-4b21-82b6-f0d1184d7fae
Portillo Vega, Maria Carmen
f913b5c5-b949-48f2-b1d0-eb7505484d5c
Galea, Ian
66209a2f-f7e6-4d63-afe4-e9299f156f0b
Cooke, Robert SC
ccfaffc5-2792-4987-91e7-584bbbf20d19
RELISH Consortium
Brown, Peter
e21132aa-29de-4c0e-846a-f090ddda0dbc
Zhou, Yaoqi
be98d1b9-cc81-4b21-82b6-f0d1184d7fae
Portillo Vega, Maria Carmen
f913b5c5-b949-48f2-b1d0-eb7505484d5c
Galea, Ian
66209a2f-f7e6-4d63-afe4-e9299f156f0b
Cooke, Robert SC
ccfaffc5-2792-4987-91e7-584bbbf20d19

Brown, Peter and Zhou, Yaoqi , RELISH Consortium (2019) Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database, 2019 (baz085). (doi:10.1093/database/baz085).

Record type: Article

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.

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Accepted/In Press date: 31 May 2019
Published date: 29 October 2019

Identifiers

Local EPrints ID: 434358
URI: http://eprints.soton.ac.uk/id/eprint/434358
PURE UUID: 637d55a9-11e4-452d-b849-15c82fc6b512
ORCID for Maria Carmen Portillo Vega: ORCID iD orcid.org/0000-0003-1583-6612
ORCID for Ian Galea: ORCID iD orcid.org/0000-0002-1268-5102

Catalogue record

Date deposited: 20 Sep 2019 16:30
Last modified: 22 Nov 2021 07:42

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Contributors

Author: Peter Brown
Author: Yaoqi Zhou
Author: Ian Galea ORCID iD
Author: Robert SC Cooke
Corporate Author: RELISH Consortium

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