The second workshop on AI + Informetrics (AII2022) affiliated with the IP&MC 2022 Annual Conference

News: Prof. Ludo Waltman ( CWTS at Leiden University) has confirmed our invitation for a keynote in AII2022.

Keynote by Prof. Ludo Waltman: Two AI flavors – Which one do we need more?

 

Purpose of the Workshop

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:

  • Cohering AI and informetrics to fulfill cross-disciplinary gaps from either theoretical or practical perspectives
  • Elaborating AI-empowered informetric models with enhanced capabilities in robustness, adaptability, and effectiveness
  • Leveraging knowledge, concepts, and models in information management to strengthen the interpretability of AI + Informetrics to adapt to empirical needs in real-world cases

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:

  • Informetrics with machine learning (including deep learning)
  • Informetrics with natural language processing or computational linguistics
  • Informetrics with computer vision
  • Informetrics with other related AI techniques (e.g., information retrieval)
  • AI for science of science
  • AI for science, technology, & innovation
  • AI for research policy and strategic management
  • Applications of AI-empowered informetrics
  •  

    Programme

    Keynote: Two AI flavors – Which one do we need more?

    Abstract: AI comes in different flavors. I will discuss the distinction between the statistical paradigm to AI and the symbolic paradigm, focusing on the role these paradigms play in the field of informetrics and scientometrics. I will reflect on the merits of the two paradigms and assess the extent to which these paradigms serve the needs of end users of scientometric analyses. I will argue that the symbolic paradigm deserves to get more attention from the scientometric community.

    Prof. Ludo Waltman is professor of Quantitative Science Studies and deputy director at the Centre for Science and Technology Studies (CWTS) at Leiden University. He is also associate director of the Research on Research Institute. His work focuses on developing new infrastructures, algorithms, and tools to support research assessment, science policy, and scholarly communication. Together with his colleague Nees Jan van Eck, Ludo has developed the well-known VOSviewer software tool for bibliometric visualization. Ludo is coordinator of the CWTS Leiden Ranking, a bibliometric ranking of major universities worldwide. He also coordinates the Initiative for Open Abstracts (I4OA). In addition, Ludo serves as Editor-in-Chief of the journal Quantitative Science Studies..


Sessions

The workshop will be held on October 21, 2022 (UTC), and specific activities include keynotes, paper presentations session.

UTC TITLE PRESENTER SESSION CHAIR
PREPARATION - Setting Up
7:30 OPENING   Yi Zhang
7:30 KEYNOTE : Two AI flavors – Which one do we need more? Ludo Waltman Yi Zhang
SESSION 1 - AI + Informetrics for Knowledge Representation and Evaluation Chengzhi Zhang
8:30 MRC-Sum: A MRC Framework for Extractive Summarization of Academic Articles [video] Shuaimin Li
8:45 A Combined Network and Text Representations for Classifying Academic Documents [video] Sahand Vahidnia
9:00 Re-examining lexical and semantic attention: Dual-view graph convolutions enhanced BERT for academic paper rating [video] Guoxiu He
9:15 RelRank: A Relevance-based Author Ranking Algorithm for Individual Publication Venues [video] Yu Zhang
30-MIN BREAK
SESSION 2 - AI + Informetrics for Prediction Yu Zhang
10:00 Incorporating Graph Embedding into User-Document Interaction for Neural Search [video] Arida Ferti Syafiandini
10:15 Towards a General Model for Technology Forecasting: An RNN Model for Scientometrics Using ArXiv Data [video] Alexander Glavackij
10:30 Want to get insight about the paper's future? A Joint Multitask Aspect Sentiment leveraged Framework for assisting Decision Prediction from Academic Peer Reviews [video] Sandeep Kumar
10:45 HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding [video] Xiaoyu Liu
SESSION 3: AI + Informetrics for Science, Technology, and Innovation Studies Wen Lou
11:00 Measuring the innovation of scientific literature through contribution sentence analysis using deep learning and cloud model [video] Haoxuan Zhang
11:15  Identifying underlying influential factors in information diffusion process: An Altmetrics approach [video] Zhen Yan
11:30  Exploring science-technology linkages: A deep learning-empowered solution [video] Yijie Cai
11:45 The diversity of canonical and ubiquitous progress in computer vision: A dynamic topic modeling approach [video] Wen Lou  
12:00 CLOSE - Wrap Up Yi Zhang, Chengzhi Zhang, Philipp Mayr, Arho Suominen, and Ying Ding

Call for Papers

You are invited to participate the the second workshop on AI + Informetrics (AII2022) affiliated with the IP&MC 2022, on 20-23, October, 2022, in Xiamen, China.

Important Dates

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


 

Special Issue & 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

Organising Committee

yi_zhang_picture.jpeg Yi Zhang (yi.zhang@uts.edu.au) is a Senior Lecturer at the Australian Artificial Intelligence Institute, 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 100 publications. His current research interests align with bibliometrics, text analytics, and information systems. He serves as an Associate Editor for Technological Forecasting & Social Change, and Scientometrics. He is the Advisory Board Member of Elsevier's International Center for the Study of Research. (https://www.uts.edu.au/staff/yi.zhang)


chengzhi_zhang_picture.png Chengzhi Zhang (zhangcz@njust.edu.cn) 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/)


philip_pmayr_picture.jpg Philipp Mayr ( philipp.mayr@gesis.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_picture 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_pictureYing 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.

Programme Committee

  • Alireza Abbasi, UNSW Canberra
  • Andrea Scharnhorst, DANS-KNAW
  • Arash Hajikhani, VTT Technical Research Centre of Finland
  • Bart Thijs, ECOOM, MSI, K.U.Leuven
  • Chao Min, Nanjing University
  • Chao Lu, Hohai University
  • Dietmar Wolfram, University of Wisconsin-Milwaukee
  • Dongbo Wang, Nanjing Agricultural University
  • Guillaume Cabanac, IRIT - Université Paul Sabatier Toulouse 3
  • Haihua Chen, University of North Texas
  • Hongshu Chen, Beijing Institute of Technology
  • Iana Atanassova, CRIT, Université de Bourgogne Franche-Comté
  • Jian Xu, Sun Yat-sen university
  • Jian Du, Peking University
  • Jin Mao, Wuhan University
  • Kai Li, Renmin University of China
  • Marc Bertin, Université Claude Bernard Lyon 1
  • Mengjia Wu, University of Technology Sydney
  • Philipp Schaer, TH Köln (University of Applied Sciences)
  • Qingqing Zhou, Nanjing Normal University
  • Shuo Xu, Beijing University of Technology
  • Vivek Kumar Singh, Banaras Hindu University, Varanasi, U.P., India
  • Xiaolan Wu, Nanjing Normal University
  • Xuefeng Wang, Beijing Institute of Technology
  • Yi Bu, Peking University
  • Ying Guo, China University of Political Science and Law
  • Ying Huang, Wuhan University
  • Yingyi Zhang, Nanjing University of Science and Technology
  • Yuzhuo Wang, Nanjing University of Science and Technology
  • Yuya Kajikawa, Tokyo University of Technology

 

Website

https://ai-informetrics.github.io/2022

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.

Links

Related Workshops
BIRNDL 2019The 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries
Venue: SIGIR 2019 in Paris, France
Proceedings: http://ceur-ws.org/Vol-2414/ 

SDP 2020First Workshop on Scholarly Document Processing
Venue: 2020 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP 2020)
Website: https://ornlcda.github.io/SDProc/ 

EEKE 2021The 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021)
Venue: ACM/IEEE Joint Conference on Digital Libraries 2021 (JCDL2021)
Website: https://eeke-workshop.github.io/2021/

AII 2021:First Workshop on AI + Informetrics (AII2021)
Venue: iConference2021
Website: https://ai-informetrics.github.io/