TY - JOUR
T1 - Large expert-curated database for benchmarking document similarity detection in biomedical literature search
AU - RELISH Consortium
AU - Brown, Peter
AU - Zhou, Yaoqi
AU - Tan, Aik-Choon
AU - el-Esawi, Mohamed A.
AU - Liehr, Thomas
AU - Blanck, Oliver
AU - Gladue, Douglas P.
AU - Almeida, Gabriel M. F.
AU - Cernava, Tomislav
AU - Sorzano, Carlos O.
AU - Yeung, Andy W. K.
AU - Engel, Michael S.
AU - Chandrasekaran, Arun R.
AU - Muth, Thilo
AU - Staege, Martin S.
AU - Daulatabad, Swapna V.
AU - Widera, Darius
AU - Zhang, Junpeng
AU - Meule, Adrian
AU - Honjo, Ken
AU - Pourret, Olivier
AU - Yin, Cong-Cong
AU - Zhang, Zhongheng
AU - Cascella, Marco
AU - Flegel, Willy A.
AU - Goodyear, Carl S.
AU - van Raaij, Mark J.
AU - Bukowy-Bieryllo, Zuzanna
AU - Campana, Luca G.
AU - Kurniawan, Nicholas A.
AU - Lalaouna, David
AU - Hüttner, Felix J.
AU - Ammerman, Brooke A.
AU - Ehret, Felix
AU - Cobine, Paul A.
AU - Tan, Ene-Choo
AU - Han, Hyemin
AU - Xia, Wenfeng
AU - McCrum, Christopher
AU - Dings, Ruud P. M.
AU - Marinello, Francesco
AU - Nilsson, Henrik
AU - Nixon, Brett
AU - Voskarides, Konstantinos
AU - Yang, Long
AU - Gurney-Champion, Oliver J.
AU - Gruber, Jens
AU - Mamoulakis, Charalampos
AU - van den Bos, Wouter
AU - Zhao, Jing
AU - Costa, Vincent D.
AU - Bengtsson-Palme, Johan
AU - Chinapaw, Mai
AU - Huirne, Judith
AU - Min, Rogier
AU - Manstead, A.S.R.
AU - Molleman, L.
AU - Bathelt, J.
AU - Kievit, R.A.
AU - Nguyen, T.T.H.
N1 - A correction has been published: Database, Volume 2020, 2020, baz138.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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 science.
AB - 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 science.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082592913&origin=inward
UR - http://www.scopus.com/inward/record.url?scp=85082592913&partnerID=8YFLogxK
UR - https://doi.org/10.1093/database/baz138
UR - https://figshare.com/projects/RELISH-DB/60095
U2 - https://doi.org/10.1093/database/baz085
DO - https://doi.org/10.1093/database/baz085
M3 - Article
C2 - 33326193
SN - 1758-0463
VL - 2019
SP - 1
EP - 67
JO - DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
JF - DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
M1 - baz085
ER -