Driven by the big data boom, informetrics, known as the study of quantitative aspects of information, has gained significant benefits from artificial intelligence – including a wide range of intelligent agents through techniques such as neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes network, planning, and language understanding. With its capacities in analyzing unstructured scalable data and streams, understanding uncertain semantics, and developing robust and repeatable models, “Artificial Intelligence + Informetrics (AI + Informetrics)” has demonstrated enormous success in turning big data into big value and impact by handling diverse challenges raised from multiple disciplines and research areas. Examples of recent work include bibliometric-enhanced information retrieval (Mayr et al., 2014), patent mapping with unsupervised learning approaches (Suominen et al., 2017), intelligent bibliometrics for tracking technological change with streaming data analytics (Zhang et al., 2017), and evaluating emerging technologies with network analytics (Zhang et al., 2021), entity extraction with full-text analytics (Wang & Zhang, 2020), and deep learning-empowered models for metadata analysis (Safder et al., 2020) and classification (Haneczok & Piskorski, 2020). Such endeavors with broadened perspectives from machine intelligence would portend far-reaching implications for science (Fortunato et al., 2018).
As a rising interest of not only the community of information management but also broad business disciplines in science and technology management, developing and applying robust computational models for analyzing large-scale scientific documents (e.g., research articles, patents, academic proposals, technical reports, and social media) with extensive uses of bibliographical indicators (e.g., citations, word semantics, and authorships) are attracting great attention. Deliverables in line with the topic may include novel methods and techniques and empirical insights for science policy, strategic management, research and development plans, and entrepreneurship.
Aiming to further gather researchers and practical users to open a collaborative platform for exchanging ideas, sharing pilot studies, and scoping future directions on this cutting-edge venue, the topic of AI + Informetrics will be a special track associated with the Information Processing and Management Conference (IP&MC) 2022. This special track is to run with the core of the information science community, but with a cross-disciplinary vision hosting researchers from computer science, library science, communication, and broad disciplines in management sciences (e.g., innovation and technology management, public administration, and information systems). This special track is to particularly target certain unsolved issues in AI + Informetrics and a wide range of its practical scenarios, specifically:
This workshop is to gather researchers and practical users to open a collaborative platform for exchanging ideas, sharing pilot studies, and scoping future directions on this cutting-edge venue. We highlight “AI + Informetrics” as endeavors in constructing fundamental theories, developing novel methodologies, bridging conceptual knowledge with practical uses, and creating real-word solutions. Interests to this workshop include, but are not limited to, the following topics:
|Online submission system is open||January 5, 2022|
|Thematic track manuscript submission due date; authors are welcome to submit early as reviews will be rolling||June 15, 2022|
|Author notification||July 31, 2022|
|IP&MC conference presentation and feedback||October 20-23, 2022|
|Post conference revision due date but authors welcome to submit earlier||January 1, 2023|
Submit your manuscript to the Special Issue category (VSI: IPMC2022 AI+INFO) through the online submission system of Information Processing & Management: https://www.editorialmanager.com/ipm/
Authors will prepare the submission following the Guide for Authors on IP&M journal at (https://www.elsevier.com/journals/information-processing-and-management/0306-4573/guide-for-authors). All papers will be peer-reviewed following the IP&MC2022 reviewing procedures.
The authors of accepted papers will be obligated to participate in IP&MC 2022 and present the paper to the community to receive feedback. The accepted papers will be invited for revision after receiving feedback on the IP&MC 2022 conference. The submissions will be given premium handling at IP&M following its peer-review procedure and, (if accepted), published in IP&M as full journal articles, with also an option for a short conference version at IP&MC2022.
Please see this infographic for the manuscript flow: https://www.elsevier.com/__data/assets/pdf_file/0003/1211934/IPMC2022Timeline10Oct2022.pdf
For more information about IP&MC2022, please visit:https://www.elsevier.com/events/conferences/information-processing-and-management-conference
Yi Zhang (firstname.lastname@example.org) is a Lecturer at the Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received dual PhD degrees, one from Beijing Institute of Technology, China and the other from UTS. He has authored more than 50 publications. His current research interests align with bibliometrics, text analytics, and information systems. He serves as diverse roles (e.g., Associate Editor, Editorial Board Member, and Managing Guest Editor) for one IEEE Trans and four other international journals. He is also a PC Member of several international conferences. (https://www.uts.edu.au/staff/yi.zhang)
Chengzhi Zhang (email@example.com) is a professor of Department of Information Management, Nanjing University of Science and Technology, China. He received his PhD degree of Information Science from Nanjing University, China. He has published more than 100 publications, including JASIST, Aslib JIM, JOI, OIR, SCIM, ACL, NAACL, etc. His current research interests include scientific text mining, knowledge entity extraction and evaluation, social media mining. He serves as Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, Aslib JIM, OIR, TEL, IDD, NLE, JDIS, DIM, DI, etc.) and PC members of several international conferences in fields of natural language process and scientometrics. (https://chengzhizhang.github.io/)
Philipp Mayr ( firstname.lastname@example.org) is a team leader at the GESIS - Leibniz-Institute for the Social Sciences department Knowledge Technologies for the Social Sciences (WTS). He received his PhD in applied informetrics and information retrieval from the Berlin School of Library and Information Science at Humboldt University Berlin. He has published in top conferences and prestigious journals in the areas informetrics, information retrieval and digital libraries. His research group focuses on methods and techniques for interactive information retrieval and data set search. He was the main organizer of the BIR workshops at ECIR 2014-2020 and the BIRNDL workshops at JCDL 2016 and SIGIR 2017-2019. (https://philippmayr.github.io/)
Arho Suominen (Arho.Suominen@vtt.fi) is Principal Scientist at the VTT Technical Research Centre of Finland and Industrial professor at Tampere University (Finland). Dr. Suominen’s research focuses on qualitative and quantitative assessment of innovation systems with a special focus on quantitative methods. His prior research has been funded by the European Commission via H2020, Academy of Finland, Finnish Funding Agency for Technology, Turku University Foundation and the Fulbright Center Finland. Through the Fulbright program, he worked as Visiting Scholar at the School of Public Policy at the Georgia Institute of Technology. Dr. Suominen has a Doctor of Science (Tech.) degree from the University of Turku and holds an Officers basic degree from the National Defence University of Finland. (https://cris.vtt.fi/en/persons/arho-suominen)
Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences, and serves as the editorial board member for several top journals in Information Science and Semantic Web. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies. (https://yingding.ischool.utexas.edu/)
All questions about submissions should be emailed to Organizing Committee.
Fortunato, S., …, et al., 2018. Science of science. Science, 359(6379).
Haneczok, J., & Piskorski, J. (2020). Shallow and deep learning for event relatedness classification. Information Processing & Management, 57(6), 102371.
Mayr, P., …, et al., 2014, April. Bibliometric-enhanced information retrieval. In European Conference on Information Retrieval (pp. 798-801). Springer, Cham.
Safder, I., Hassan, S. U., Visvizi, A., Noraset, T., Nawaz, R., & Tuarob, S. (2020). Deep learning-based extraction of algorithmic metadata in full-text scholarly documents. Information Processing & Management, 57(6), 102269.
Suominen, A., Toivanen, H., & Seppänen, M. (2017). Firms’ knowledge profiles: Mapping patent data with unsupervised learning. Technological Forecasting and Social Change, 115, 131-142.
Wang, Y., & Zhang, C. (2020). Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing. Journal of Informetrics, 14(4), 101091.
Zhang, Y., …, et al., 2017. Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics. Journal of the Association for Information Science and Technology, 68(8), pp.1925-1939.
Zhang, Y., …, et al., 2021. Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies. Journal of Informetrics, 15(4), 101202.
BIRNDL 2019：The 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries
Venue: SIGIR 2019 in Paris, France
SDP 2020：First Workshop on Scholarly Document Processing
Venue: 2020 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP 2020)
EEKE 2021：The 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021)
Venue: ACM/IEEE Joint Conference on Digital Libraries 2021 (JCDL2021)
AII 2021：First Workshop on AI + Informetrics (AII2021)