Special Issue on “Scholarly literature mining
with Information Retrieval and Natural Language Processing”
Accepted papers appeared in a Special Issue in Scientometrics, Volume 125, issue 3, December 2020. See links below. Preface: https://doi.org/10.1007/s11192-020-03763-4
Cabanac, G., Frommholz, I. & Mayr, P. Scholarly literature mining with information retrieval and natural language processing: Preface. Scientometrics (2020). https://doi.org/10.1007/s11192-020-03763-4
Haunschild, R., & Marx, W. (2020). Discovering seminal works with marker papers. Scientometrics. https://doi.org/10.1007/s11192-020-03358-z
Saier, T. & Färber, M. (2020). unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. Scientometrics. https://doi.org/10.1007/s11192-020-03382-z
Christin Katharina Kreutz, Premtim Sahitaj, Ralf Schenkel (2020). Evaluating semantometrics from computer science publications. Scientometrics. https://doi.org/10.1007/s11192-020-03409-5
Chrysoula Zerva, Minh-Quoc Nghiem, Nhung T.H. Nguyen, Sophia Ananiadou (2020). Cited text span identification for scientific summarisation using pre-trained encoders. Scientometrics. https://doi.org/10.1007/s11192-020-03455-z
Jean-Charles Lamirel, Yue Chen, Pascal Cuxac, Shadi Al Shehabi, Nicolas Dugue, Liu Zeyuan (2020). An overview of the history of Science of Science in China based on the use of bibliographic and citation data: a new method of analysis based on clustering with feature maximization and contrast graphs. Scientometrics. https://doi.org/10.1007/s11192-020-03503-8
Jason Portenoy, Jevin West (2020). Constructing and evaluating automated literature review systems. Scientometrics. https://doi.org/10.1007/s11192-020-03490-w
André Greiner-Petter, Abdou Youssef, Terry Ruas, Bruce R. Miller, Moritz Schubotz, Akiko Aizawa, Bela Gipp (2020). Math-Word Embedding in Math Search and Semantic Extraction. Scientometrics. https://doi.org/10.1007/s11192-020-03502-9
Haiko Lietz (2020). Drawing impossible boundaries: Field delineation of Social Network Science. Scientometrics. https://doi.org/10.1007/s11192-020-03527-0
Moreno La Quatra, Luca Cagliero, Elena Baralis (2020). Exploiting pivot words to classify and summarize discourse facets of scientific papers. Scientometrics. https://doi.org/10.1007/s11192-020-03532-3
Sergio Jimenez, Youlin Avila, George Duenas, Alexander Gelbukh (2020). Automatic prediction of citability of scientific articles by stylometry of their titles and abstracts. Scientometrics. https://doi.org/10.1007/s11192-020-03526-1
Ahmed AbuRa'ed, Horacio Saggion, Alexander Shvets, Álex Bravo (2020). Automatic related work section generation: experiments in scientific document abstracting. Scientometrics. https://doi.org/10.1007/s11192-020-03630-2
Andres Carvallo, Denis Parra, Hans Löbel, Alvaro Soto (2020). Automatic Document Screening of Medical Literature Using Word and Text Embeddings on an Active Learning Setting. Scientometrics. https://doi.org/10.1007/s11192-020-03648-6
Rodrigo Nogueira, Zhiying Jiang, Kyunghyun Cho, Jimmy Lin (2020). Navigation-Based Candidate Expansion and Pretrained Language Models for Citation Recommendation. Scientometrics. https://doi.org/10.1007/s11192-020-03718-9
Jodi Schneider, Di Ye, Alison M Hill, Ashley S Whitehorn (2020). Continued Post-Retraction Citation of a Fraudulent Clinical Trial Report, Eleven Yearsafter it was retracted for Falsifying Data. Scientometrics. https://doi.org/10.1007/s11192-020-03631-1
Overview and Aim
Searching for scientific information is a long-lived information need. In the early 1960s, Salton (1963) was already striving to enhance information retrieval by including clues inferred from bibliographic citations. The development of citation indexes pioneered by Garfield (1955) proved determinant for such a research endeavour at the crossroads between the nascent fields of Information Retrieval (IR) and Bibliometrics. Here bibliometrics refers to the statistical analysis of the academic literature (Pritchard, 1969) and plays a key role in scientometrics: the quantitative analysis of science and innovation (Leydesdorff & Milojevic, 2015). The pioneers who established these fields in Information Science—such as Salton and Garfield—were followed by scientists who specialised in one of these (White & McCain, 1998), leading to the two loosely connected fields we know of today.
This Special Issue of the Scientometrics journal pursues the goal of the BIR workshop series founded in 2014: tightening up the link between IR and Bibliometrics. This Special Issue follows a recent issue on the same topic in Scientometrics (Cabanac et al., 2018). We have been striving to get the ‘retrievalists’ and ‘citationists’ (White & McCain, 1998) active in both academia and the industry together, who are developing search engines and recommender systems such as ArnetMiner, CiteSeerX, DocEar, Google Scholar, Microsoft Academic Search, and Semantic Scholar, just to name a few. These bibliometric-enhanced IR systems must deal with the multifaceted nature of scientific information by searching for or recommending academic papers, patents, venues (i.e., conferences or journals), authors, experts (e.g., peer reviewers), references (to be cited to support an argument), and datasets. The underlying models harness relevance signals from keywords provided by authors, topics extracted from the full-texts, coauthorship networks, citation networks, and various classifications schemes of science.
Bibliometric-enhanced IR is a hot topic whose recent developments made the news—see for instance the Initiative for Open Citations (Shotton, 2018) and the Google Dataset Search (Castelvecchi, 2018) launched on September 4, 2018. We believe that venues like BIR and BIRNDL are much needed scientific events for the ‘retrievalists’ and ‘citationists’ to meet and join forces pushing the knowledge boundaries of IR applied to literature search and recommendation. We invite BIR and BIRNDL authors, as well as all “B+IR researchers” at-large working at the crossroads between bibliometrics and information retrieval to submit to the B+IR Special Issue of Scientometrics.
B+IR: Topics of Interest
We especially welcome submissions regarding all three aspects of the search/recommendation process:
User needs and behaviour regarding scientific information, such as:
Finding relevant papers/authors for a literature review.
Measuring the degree of plagiarism in a paper.
Identifying expert reviewers for a given submission.
Flagging predatory conferences and journals.
The characteristics of scientific information, such as:
Measuring the reliability of bibliographic libraries.
Spotting research trends and research fronts.
Academic search/recommendation systems, such as:
Modelling the multifaceted nature of scientific information.
Building test collections for reproducible BIR.
System support for literature search and recommendation.
Submission Details and Deadlines
September 30, 2019: All submissions due
1st round of reviews
November 30, 2019: Referees' reports with decision (accept, minor/major modifications, reject) sent to authors
January 30, 2020: Revised manuscripts due
2nd round of reviews
February 29, 2020: Referees' 2nd reports with decision (accept, minor/major modifications, reject) sent to authors
March 30, 2020: Revised manuscripts due
Online publication with citable DOI in Early View: 3-4 weeks upon acceptance
Publication in the Special Issue of Scientometrics: Autumn 2020
Cabanac, G., Frommholz, I., & Mayr, P. : Bibliometric-enhanced information retrieval: Preface. Scientometrics, 116(2), 1225–1227 (2018). doi:10.1007/s11192-018-2861-0
Castelvecchi, D.: Google unveils search engine for open data [News & Comment]. Nature (2018). doi:10.1038/d41586-018-06201-x
Garfield, E.: Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111 (1955). doi:10.1126/science.122.3159.108
Leydesdorff, L., Milojević, S.: Scientometrics. In: Wright, J.D. (ed.) International Encyclopedia of the Social & Behavioral Sciences, vol. 21, pp. 322–327. Elsevier, 2nd edn. (2015). doi:10.1016/b978-0-08-097086-8.85030-8
Pritchard, A.: Statistical bibliography or bibliometrics? [Documentation notes]. Journal of Documentation, 25(4), 348–349 (1969). doi:10.1108/eb026482
Salton, G.: Associative document retrieval techniques using bibliographic information. Journal of the ACM, 10(4), 440–457 (1963). doi:10.1145/321186.321188
Shotton, D.: Funders should mandate open citations. Nature, 553(7687), 129 (2018). doi:10.1038/d41586-018-00104-7
White, H.D., McCain, K.W.: Visualizing a discipline: An author co-citation analysis of Information Science, 1972–1995. Journal of the American Society for Information Science, 49(4), 327–355 (1998). doi:b57vc7