1st Workshop on AI + Informetrics (AII2021) at the iConference2021, Virtual
Purpose of the Workshop
Driven by the big data boom, informetrics, known as the study of quantitative aspects of information, has gained great benefits from artificial intelligence
(Nilsson 1998) – 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”
has demonstrated enormous success in turning big data into big value
and impact by handling diverse challenges raised from multiple
disciplines and research areas. For example, bibliometric-enhanced
information retrieval (Mayr et al., 2014), science mapping with topic
models (Suominen and Toivanen, 2016), streaming data analytics for
tracking technological change (Zhang et al., 2017), and entity
extraction with unsupervised machine learning techniques (Zhang and
Zhang, 2019). Such endeavours with broadened perspectives from machine
intelligence would portend far-reaching implications for science
(Fortunato et al., 2018), but how to effectively cohere the power of AI
and informetrics to create cross-disciplinary solutions is still elusive
from neither theoretical nor practical perspectives.
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.
Call for Papers
You are invited to participate in the 1st Workshop on AI + Informetrics (AII2021) to be held as a virtual event as part of the iConference2021, Virtual, on March 28-31, 2021. See https://ischools.org/Program
Interests to this workshop include, but 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-enhanced informetrics
All papers should be submitted as PDF files to EasyChair.
All papers must be original and not simultaneously submitted to another
journal or conference. The following paper categories are welcome:
All submissions must be written in English, following Springer’s prescribed LNCS template. and should be submitted as PDF files to EasyChair.
We accept two types Regular Papers:
Full Research Papers: Up to 6,000 words, excluding references.
Short Research Papers: Up to 3,000 words, excluding references.
We welcome submissions detailing original, early findings, works in progress and industrial applications of “artificial intelligence + informetrics” for a special poster/demo session, possibly with a 3-minute presentation in the main session. Poster/demo submissions should be vivid, with brief textual descriptions.
All poster/domo abstracts must follow Springer’s prescribed LNCS template. Abstracts can be up to 2,500 words in length (excluding references). Abstracts must be fully anonymized.
All dates are Anywhere on Earth (AoE).
Submission deadline: Feb 1, 2021
Notification date: Feb 28, 2021
Final camera-ready versions due: March 7, 2021
Review Process & Proceedings
All submissions will be reviewed by at least two independent
reviewers. Please be aware of the fact that once the paper is accepted,
at least one author per paper needs to register for the workshop and
attend the workshop to present the work. In light of the recent events
regarding the Coronavirus, AII2021 will be an all-virtual workshop as
iConference will be online only.
Workshop proceedings will be deposited online in the CEUR workshop proceedings publication service. This way the proceedings will be permanently available and citable (digital persistent identifiers and long-term preservation).
Accepted submissions are eligible for submitting to our special issue in Scientometrics.
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, 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)
All questions about submissions should be emailed to Organizing Committee.
Fortunato, S., et al., 2018. Science of science. Science, 359(6379).
Nilsson, N.J., 1998. Artificial intelligence: A new synthesis. Morgan Kaufmann.
Mayr, P., et al., 2014, April. Bibliometric-enhanced information retrieval. In European Conference on Information Retrieval (pp. 798-801). Springer, Cham.
Suominen, A. and Toivanen, H., 2016. Map of science with topic modeling: Comparison of unsupervised learning and human‐assigned subject classification. Journal of the Association for Information Science and Technology, 67(10), pp.2464-2476.
Zhang, Y. and Zhang, C., 2019. Unsupervised keyphrase extraction in academic publications using human attention. 17th International Conference on Scientometrics and Informetrics (ISSI 2019), Rome, Italy.
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.
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 2020：First Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents
Venue: ACM/IEEE Joint Conference on Digital Libraries 2020 (JCDL2020)