AI + Informetrics: Robust Models for Large-scale Analytics - Information Processing and Management Conference - Elsevier Search Support View Cart

Note: This special issue is a Thematic Track at IP&MC2022. For more information about IP&MC2022, please visit https://www.elsevier.com/events/conferences/information-processing-and-management-conference.

Title of the Special Issue

AI + Informetrics: Robust Models for Large-scale Analytics (VSI: IPMC2022 AI+INFO)

Guest Editors

Yi Zhang (yi.zhang@uts.edu.au)Senior Lecturer at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.

Chengzhi Zhang (zhangcz@njust.edu.cn)Professor at the Department of Information Management, Nanjing University of Science and Technology, China.

Philipp Mayr (philipp.mayr@gesis.org)Team Leader at the Department of Knowledge Technologies for the Social Sciences, GESIS – Leibniz Institute for the Social Sciences, Germany.

Arho Suominen (Arho.Suominen@vtt.fi)Principal Scientists at the VTT Technical Research Centre of Finland, & Industrial Professor at the Tampere University, Industrial Engineering, Finland.

Ying Ding (ying.ding@ischool.utexas.edu)Bill & Lewis Professor at the School of Information & Dell Medical School, University of Texas at Austin, USA

Background

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:

Possible Topics of Submissions

Interests to this special track include, but are not limited to, the following topics:

Important Dates

Submission Guidelines

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

Program Committee for the Special Track

References

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.