1st Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2020) at the ACM/IEEE Joint Conference on Digital Libraries 2020 (JCDL2020), Xi'an, China 

第一届科学文献的知识实体抽取与评估研讨会(EEKE2020)，作为在中国西安举行的ACM/IEEE数字图书馆联合会议(JCDL2020)的一部分，发布征文启事

官网：https://eeke2020.github.io/

目的

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 [1]. As a crucial medium for knowledge transmission, scientific documents that contain a large number of knowledge entities attract the attention of scholars [2]. 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 [3], which aims to develop next-generation applications of artificial intelligence. 

在大数据时代，海量的信息和数据极大地改变了人类文明。信息的广泛可得性为人们提供了更多的机会，但也出现了一个新的挑战:我们如何从众多的信息源获取有用的知识。知识实体是某一特定学科或研究领域中相对独立、完整的知识模块。作为知识传播的重要媒介，包含大量知识实体的科学文献受到学者的关注。在科学文献中，知识实体是指作者提及或引用的知识，如算法、模型、理论、数据集、软件等，反映了作者在解决问题时所使用的各种资源。准确、全面地提取科学文献中的知识实体成为一个重要的课题。我们可以向学者推荐与给定知识实体(如LSTM模型)相关的文档，特别是对某个研究领域的初学者。DARPA最近启动了ASKE(自动科学知识提取)项目[3]，旨在开发下一代人工智能应用。

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 [4] and domain knowledge-graphs [5]. 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) [6] or Workshop on Mining Scientific Publications (WOSP) [7], 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.

因此，本次研讨会的目标是让相关团体参与到从科学文献中提取和评估知识实体的公开问题中来。目前，学者们已经使用知识实体来构建一般知识图谱[4]和领域知识图谱[5]。这些研究的数据来源包括文本(新闻、政策文件、电子邮件等)和多媒体(视频、图像等)数据。与现有的研究与研讨会相比，比如 Bibliometric-enhanced Information Retrieval 和Natural Language Processing for Digital Libraries (BIRNDL) [6] 以及 Workshop on Mining Scientific Publications (WOSP) [7]，这个研讨会旨在从科学文档中提取知识实体,实体的特点,探索实际应用。本次研讨会的成果将为学者，特别是早期职业研究者提供知识推荐和其他基于知识实体的服务。

主题

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:

本次研讨会将面向计算机和信息科学领域的学者，主要研究信息提取、文本挖掘、NLP、IR和数字图书馆。它对出版物渠道中的所有利益相关者:实施者、出版者和决策者也很重要。本次研讨会将为知识实体的提取和评估提供最前沿、跨学科的方向，强调在科学文献中识别知识断言的智能方法的发展，并促进了知识实体的应用。我们邀请有启发性的研究，包括但不限于，知识实体的提取方法和知识实体的应用。感兴趣的领域的具体例子包括

TBD（待定）

(The workshop will last two half-days and specific activities include keynotes, paper presentations and a poster & demonstration session.)

(研讨会为期两天半，具体活动包括主题演讲、论文展示和海报及演示。)

Regular papers: 

All submissions must be written in English, following the ACM Proceedings template (10 pages for full papersand 4 pages for short papers exclusive of unlimited pages for references) and should be submitted as PDF files to EasyChair.

所有提交的论文必须按照ACM会议记录模板(10页为全文，4页为短文，不包括不限页数的参考资料)用英文书写，并以PDF格式提交至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).

我们欢迎提交详细介绍原始的、早期的发现、正在进行的工作以及知识实体提取和评估的工业应用的专题海报，可能在主论坛上有2分钟的展示。一些研究轨道论文也将被邀请到海报轨道代替，虽然在最后的过程中，海报和研究轨道提交没有区别。这些论文应遵循与研究跟踪论文相同的格式，但可以更短(2页的海报和演示论文)。

Submit a paper

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 light of the recent events regarding the Coronavirus, the workshop follows attendance policy of JCDL2020, namley: 1) allowing virutal attendance / presentation of papers, and 2) not enforcing the "no show" policy.

所有参赛作品将由至少两名独立评审员进行评审。请注意，每篇论文至少有一名作者需要登记参加研讨会，并出席研讨会来展示作品。鉴于最近关于冠状病毒的事件，研讨会遵循JCDL2020的出勤政策，1)出勤/提交论文，2)不执行“不出勤”政策。

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).

研讨会论文集将在线存放在欧洲欧元研讨会论文集出版服务中。通过这种方式，程序将是永久可用和可编辑的(数字持久标识符和长期保存)。

All dates are Anywhere on Earth (AoE).

Deadline for submission: May 31, 2020

提交截止日期:2020年5月31日

Notification of acceptance: July 1, 2020

录用通知:2020年7月1日

Camera ready: July, 22, 2020

完稿：2020年7月22日

Workshop: August 4-5, 2020

会议时间：2020年8月4日-5日

Chengzhi Zhang (zhangcz@just.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 JASIT, Aslib Journal of Information Management, ACL, NAACL, etc. His current research interests include scientific text mining, knowledge entity extraction and evaluation, review mining. He serves as Editorial Board Member and Managing Guest Editor for 5 international journals and PC members of several international conferences in fields of natural language process and scientometrics.

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.

Wei Lu (weilu@whu.edu.cn) is a professor of School of Information Management and director of Information Retrieval and Knowledge Mining Center. 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.

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.

1.Chang, X., & Zheng, Q. (2007). Knowledge element extraction for knowledge-based learning resources organization. In International Conference on Web-Based Learning (pp. 102-113). Springer, Berlin, Heidelberg.

2.Ying, D., Min, S., Jia, H., Qi, Y., Erjia, Y., Lili, L., Tamy, C. Entitymetrics: Measuring the Impact of Entities. Plos One, 2013, 8(8), e71416.

3.https://www.darpa.mil/program/automating-scientific-knowledge-extraction

4.Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). Dbpedia: A nucleus for a web of open data. In The semantic web (pp. 722-735). Springer, Berlin, Heidelberg.

5.http://www.geonames.org/

6.Cabanac, G., Chandrasekaran, M. K., Frommholz, I., Jaidka, K., Kan, M. Y., Mayr, P., & Wolfram, D. (2017). Report on the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016). In ACM SIGIR Forum (Vol. 50, No. 2, pp. 36-43). New York, NY, USA: ACM.

7.https://wosp.core.ac.uk/lrec2018/

8.Boland, K., & Krüger, F. (2019). Distant supervision for silver label generation of software mentions in social scientific publications. In Proceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (pp. 15-27).

9.Zha, H., Chen, W., Li, K., & Yan, X. (2019). Mining Algorithm Roadmap in Scientific Publications. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1083-1092).

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

Organizing Committee: Muthu Kumar Chandrasekaran, Philipp Mayr, Dayne Freitag, Min-Yen Kan.

Proceedings: http://ceur-ws.org/Vol-2414/