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

Submission Guidelines

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:
       

Regular Papers
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.

Posters/Demo
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.

Submit a paper        

Important Dates

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).

Special Issues

Accepted submissions are eligible for submitting to our special issue in Scientometrics.

Organising Committee

Yi Zhang (yi.zhang@uts.edu.au) 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 (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, 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 ( 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 (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.

Programme Committee

• TBD

Website

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

References

1. Fortunato, S., et al., 2018. Science of science. Science, 359(6379).

2. Nilsson, N.J., 1998. Artificial intelligence: A new synthesis. Morgan Kaufmann.

3. Mayr, P.,  et al., 2014, April. Bibliometric-enhanced information retrieval. In European Conference on Information Retrieval (pp. 798-801). Springer, Cham.

4. 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.

5. 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.

6. 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.

Links

Related  Workshops：
         

• BIRNDL  2019：The 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 2020：First Workshop on Scholarly Document Processing
  Venue:  2020 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP 2020)
  Website: https://ornlcda.github.io/SDProc/ 

• EEKE 2020：First Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents
  Venue: ACM/IEEE Joint Conference on Digital Libraries 2020 (JCDL2020)
  Website: https://eeke2020.github.io/