8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019)

CEUR workshop proceedings are published: http://ceur-ws.org/Vol-2345/

Keynote by Dr. Iana Atanassova, CRIT, Université de Bourgogne Franche-Comté, France
Title of the talk: Beyond Metadata: the New Challenges in Mining Scientific Papers

Abstract: Scientific articles make use of complex argumentative structures whose exploitation from a computational point of view is an important challenge. The exploration of scientific corpora involves methods and techniques from Natural Language Processing in order to develop applications in the field of Information Retrieval, Automatic Synthesis, citation analyses or ontological population. Among the problems that remain to be addressed in this domain is the developing fine-grained analysis of the text content of articles to identify specific semantic categories such as the expression of uncertainty and controversy that are an integral part of the scientific process. The well-known IMRaD structure (Introduction, Methods, Results, and Discussion) is standard template that governs the structure of articles in experimental sciences and provides clearly identifiable text units. We study the internal structure of articles from several different perspectives and report on the processing of a large sample extracted from the PLOS corpus. On the one hand, we analyze citation contexts with respect to their positions, verbs used and similarities across the different sections, and on the other hand, we quantify text re-use in abstracts as well as other phenomena such as the expression of uncertainty. The production of standard datasets dedicated to such tasks is now necessary and would provide favorable environment for the development of new approaches, e.g. using neural networks that require large amounts of labeled data.

You are invited to submit to the 8th international workshop on Bibliometric-enhanced Information Retrieval (BIR 2019), to be held as part of the 41st European Conference on Information Retrieval (ECIR 2019). https://www.ecir2019.org/

• Submissions: 27 January 2019
• Notifications: 16 February 2019
• Camera Ready Contributions: 2 April 2019
• Workshop: 14 April 2019 in Cologne, Germany

The Bibliometric-enhanced Information Retrieval (BIR) workshop series at ECIR tackles issues related to academic search, at the crossroads between Information Retrieval and Bibliometrics.  BIR is a hot topic investigated by both academia (e.g., ArnetMiner, CiteSeerX, DocEar) and the industry (e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar). A one-day workshop is to be held at ECIR 2019 in Cologne, Germany.

Past BIR proceedings are online https://dblp.org/search?q=BIR.ECIR as open access.

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 [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.

The purpose of the BIR workshop series founded in 2014 is to tighten up the link between IR and Bibliometrics.  We strive 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 BIR@ECIR is a much needed scientific event for the ‘retrievalists’ and ‘citationists’ to meet and join forces pushing the knowledge boundaries of IR applied to literature search and recommendation.

• 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

We 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.

We especially invite descriptions of running projects and ongoing work as well as contributions from industry. Papers that investigate multiple themes directly are especially welcome.

• Muhammad Kamran Abbasi, University of Sindh, Pakistan
• Karam Abdulahhad, GESIS – Leibniz Institute for the Social Sciences, Germany
• Iana Atanassova, CRIT, Université de Franche-Comté, France
• Patrice Bellot, Aix-Marseille Université - CNRS (LSIS), France
• Marc Bertin, Université Lyon 1, France
• Jose Borbinha, IST / INESC-ID, Portugal
• Zeljko Carevic, GESIS - Leibniz Institute for the Social Sciences, Germany
• Muthu Kumar Chandrasekaran, National University of Singapore, Singapore
• Nicola Ferro, University of Padova, Italy
• Edward Fox, Virginia Polytechnic Institute and State University, USA
• Norbert Fuhr, University of Duisburg-Essen, Germany
• Behnam Ghavimi, GESIS – Leibniz Institute for the Social Sciences, Germany
• C. Lee Giles, The Pennsylvania State University, USA
• Bela Gipp, Bergische University Wuppertal, Germany
• Daniel Hienert, GESIS – Leibniz Institute for the Social Sciences, Germany
• Gilles Hubert, University of Toulouse, France
• Kokil Jaidka, University of Pennsylvania, USA
• Roman Kern, Know-Center GmbH, Austria
• Petr Knoth, The Open University, UK
• Marijn Koolen, Huygens Institute for the History of the Netherlands, Netherlands
• Rob Koopman, OCLC, The Netherlands
• Cyril Labbé, Grenoble University, France
• Vincent Larivière, EBSI-UdeM, Canada
• Jochen L. Leidner, Thomson Reuters, UK
• Haiming Liu, University of Bedfordshire, UK
• Norman Meuschke, University of Wuppertal, Germany
• Stasa Milojevic, Indiana University Bloomington, USA
• Peter Mutschke, GESIS – Leibniz Institute for the Social Sciences, Germany
• Wolfgang Otto, GESIS – Leibniz Institute for the Social Sciences, Germany
• Horacio Saggion, Universitat Pompeu Fabra, Spain
• Philipp Schaer, TH Cologne, Germany
• Ralph Schenkel, Trier University, Germany
• Andrea Scharnhorst, DANS-KNAW, The Netherlands
• Vivek Kumar Singh, Banaras Hindu University, India
• Henry Small, SciTech Strategies, USA
• Cassidy Sugimoto, Indiana University Bloomington, USA
• Lynda Tamine, University of Toulouse, France
• Ludovic Tanguy, University of Toulouse, France
• Simone Teufel, Cambridge University, UK
• Ulrich Thiel, Fraunhofer IPA-PAMB, Germany
• Dietmar Wolfram, University of Wisconsin-Milwaukee, USA
• Haozhen Zhao, Navigant, USA

• Guillaume Cabanac, University of Toulouse, France
• Ingo Frommholz, University of Bedfordshire in Luton, UK
• Philipp Mayr, GESIS - Leibniz Institute for the Social Sciences, Germany