3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022)
at the ACM/IEEE Joint Conference on Digital Libraries 2022 (JCDL2022), Cologne, Germany and Online
News: Prof. Alan Porter (Georgia Institute of Technology) and Mr. Nils Newman (Search Technology Inc) have confirmed our invitation for a keynote in EEKE2022.
Keynote by Prof. Alan Porter and Mr. Nils Newman: What knowledge to extract from “Tech Mining”
News: Prof. Daqing He (University of Pittsburgh) has confirmed our invitation for a keynote in EEKE2022.
Keynote by Prof. Daqing He: Keyphrases as Knowledge Units for Text-based Applications
You are invited to participate in the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022), to be held as part of the ACM/IEEE Joint Conference on Digital Libraries 2022, Cologne, Germany and Online, June 20 – 24, 2022
In the era of big data, massive amounts of information and data have dramatically changed human civilization. The broad availability of information provides more opportunities for people, but there has appeared a new challenge: how can we obtain useful knowledge from numerous information sources. A knowledge entity is a relatively independent and integral knowledge module in a special discipline or a research domain . As a crucial medium for knowledge transmission, scientific documents that contain a large number of knowledge entities attract the attention of scholars . In scientific documents, knowledge entities refer to the knowledge mentioned or cited by authors, such as algorithms, models, theories, datasets and software, which reflect the various resources used by the authors in solving problems. Extracting knowledge entities from scientific documents in an accurate and comprehensive way becomes a significant topic. We may recommend documents related to a given knowledge entity (e.g. LSTM model) for scholars, especially for beginners in a research field. DARPA has recently launched the ASKE (Automating Scientific Knowledge Extraction) project , which aims to develop next-generation applications of artificial intelligence.
Therefore, the goal of this workshop is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. At present, scholars have used knowledge entities to construct general knowledge-graphs  and domain knowledge-graphs . Data sources for these studies include text (news, policy files, email, etc.) and multimedia (video, image, etc.) data. Compared to existing research and workshops like Joint workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)  or Workshop on Mining Scientific Publications (WOSP) , this workshop aims to extract knowledge entities from scientific documents, and explore the feature of entities to conduct practical applications. The results of this workshop are expected to provide scholars, especially early career researchers, with knowledge recommendations and other knowledge entity-based services.
This workshop will be relevant to scholars in computer and information science, specialized in Information Extraction, Text Mining, NLP, IR and Digital Libraries. It will also be of importance for all stakeholders in the publication pipeline: implementers, publishers and policymakers. This workshop entitles this cutting-edge and cross-disciplinary direction Extraction and Evaluation of Knowledge Entity, highlighting the development of intelligent methods for identifying knowledge claims in scientific documents, and promoting the application of knowledge entities. We invite stimulating research on topics including, but not limited to, methods of knowledge entity extraction and applications of knowledge entity. Specific examples of fields of interest include:
Abstract: We introduce “Tech Mining” -- text analyses of R&D Information to gain useful intelligence on advancing sciences and technologies. This specialty applies some of the same tools as EEKE, as we illustrate by comparing their topical emphases. We present the generation of tech emergence scores as an illustration of an advanced Tech Mining analytical capability. Some lessons learned in the development and applications of Tech Mining may suggest EEKE possibilities that we hope to discuss.
Prof. Alan Porter is Director of R&D for Search Technology, Inc., Norcross, GA (producers of VantagePoint and Derwent Data Analyzer software). He is also Professor Emeritus of Industrial & Systems Engineering, and of Public Policy, at Georgia Tech, where he is Co-director of the Program in Science, Technology & Innovation Policy (STIP). Dr. Porter is author or co-author of some 260 articles indexed in Web of Science, and author or editor of 17 books, including Tech Mining (Wiley, 2005) and Forecasting and Management of Technology (Wiley, 2011). He co-founded the International Association for Impact Assessment and later served as president. Research interests key on developing indicators of technological emergence (with current NSF support). Publications are available at: http://www.researchgate.net/profile/Alan_Porter4. He can be reached at: email@example.com.
Mr. Nils Newman is the President of Search Technology in Norcross, Georgia, USA. For over twenty years, he has worked on the development of analytical tools to assist in the management of technology. His work focuses on the use of bibliographic and patent information in research evaluation, competitive intelligence, and strategic planning. Mr. Newman has a Bachelor of Mechanical Engineering and an MS in Technology and Science Policy from the Georgia Institute of Technology. In his spare time, he is pursuing a PhD at UNU-MERIT Maastricht.
Abstract: Natural language text is the main form of communication in various domains such as scholarly communication, student instructions, and healthcare. Keyphrases in the form of noun phrases are often identified and extracted as the knowledge unit for representing the content of natural language text, and they take various roles in contributing downstream tasks. However, we are still exploring important relevant issues such as the characteristics of keyphrases, their roles in knowledge exchange, and their usages in different domains. In this talk, I will present several research projects we conducted on exploring keyphrases as knowledge units, and their applications to different domains. My talk will cover keyphrase generation from academic papers using deep learning methods, keyphrase representation as the knowledge units in textbooks for supporting students’ learning, and keyphrase identification in the form of chief complaint recognition from clinical reports for representing patients’ symptoms and diseases. The goal of this talk is to highlight the importance of keyphrases in natural language text and to illustrate appropriate technologies for fulfilling keyphrase’s knowledge unit roles in various application domains.
Dr. Daqing He is a full professor at the Department of Informatics and Networked Systems, School of Computing and Information, the University of Pittsburgh. His main research interests cover information retrieval and access, natural language processing, adaptive and interactive system design, online academic communication and research data management. Dr. He has been the Principal Investigator (PI) and Co-PI for various research grants funded by the National Science Foundation (NSF), National Institute of Health, United States Defense Advanced Research Projects Agency (DARPA), Amazon, UPMC, OCLC/ALISE, University of Pittsburgh, and other agencies. He has published more than 200 articles in internationally recognized journals and conferences in these areas, which include Journal of Association for Information Science and Technology, Information Processing and Management, ACM Transaction on Information Systems, Journal of Information Science, IEEE Computers, ACM SIGIR, ACM CIKM, WWW, ACM CHIIR, ACM CSCW, ASIST, and so on. He services as the associate editor of “Aslib Journal of Information Management'', and on the editorial board of “Information Processing and Management”.
Regular papers: All submissions must be written in English, following the ACM Proceedings template (10 pages for full papers and 4 pages for short papers exclusive of unlimited pages for references) and should be submitted as PDF files to EasyChair.
Poster & demonstration: We welcome submissions detailing original, early findings, works in progress and industrial applications of knowledge entities extraction ande evaluation for a special poster session, possibly with a 2-minute presentation in the main session. Some research track papers will also be invited to the poster track instead, although there will be no difference in the final proceedings between poster and research track submissions. These papers should follow the same format as the research track papers but can be shorter (2 pages for poster and demo papers).
All submissions will be reviewed by at least two independent reviewers. Please be aware of the fact that at least one author per paper needs to register for the workshop and attend the workshop to present the work. In case of no-show the paper (even if accepted) will be deleted from the proceedings and from the program.
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 will be invited to submit to our special issue in Scientometrics.
All dates are Anywhere on Earth (AoE).
Deadline for submission:
May, 15, 2022 May, 18, 2022
Notification of acceptance: June, 10, 2022
Camera ready: June, 20, 2022
Workshop: June, 24, 2022
Chengzhi Zhang (firstname.lastname@example.org) 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, IPM, OIR, Aslib JIM, TEL, 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 ( email@example.com) 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 2014-2021 and the BIRNDL workshops at JCDL 2016 and SIGIR 2017-2019. (https://philippmayr.github.io/)
Wei Lu (firstname.lastname@example.org) is a professor of School of Information Management and director of Information Retrieval and Knowledge Mining Center, Wuhan University. He received his PhD degree of Information Science from Wuhan University, China. His current research interests include information retrieval, text mining, QA etc. He has papers published on SIGIR, Information Sciences, JASIT, Journal of Information Science etc. He serves as diverse roles (e.g., Associate Editor, Editorial Board Member, and Managing Guest Editor) for several journals. (http://220.127.116.11/member/4)
Yi Zhang (email@example.com) works as a Senior Lecturer at the Australian Artificial Intelligence Institute, University of Technology Sydney. He holds dual Ph.D. degrees in Management Science & Engineering and in Software Engineering. His research interests align with intelligent bibliometrics - incorporating artificial intelligence and data science techniques with bibliometric indicators for broad science, technology & innovation studies. He is the recipient of the 2019 Discovery Early Career Researcher Award granted by the Australian Research Council. He serves as the Associate Editor for Technol. Forecast. & Soc. Change, the Editorial Board Member for the IEEE Trans. Eng. Manage., and the Advisory Board Member for the International Center for the Study of Research. (https://www.uts.edu.au/staff/yi.zhang)
BIRNDL 2019：4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries
Venue: SIGIR 2019 in Paris, France
SDP 2020：1st Workshop on Scholarly Document Processing
Venue: 2020 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP 2020)
EEKE 2021：2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents
Venue: ACM/IEEE Joint Conference on Digital Libraries 2021 (JCDL2021)
AII 2021：1st Workshop on AI + Informetrics (AII2021)