Special Issue: Extraction and Evaluation of Knowledge Entities from Scientific Documents

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 special issue (SI) 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. This SI aims to extract knowledge entities from scientific documents, and explore the feature of entities to conduct practical applications. The results of this SI are expected to provide scholars, especially early career researchers, with knowledge recommendations and other knowledge entity-based services.

This SI 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 SI 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:

• Extraction knowledge and entity from scientific documents • Model and algorithmize entity extraction from scientific documents • Dataset and metrics mention extraction from scientific documents • Software and tool extraction from scientific documents • Construction of a knowledge entity graph and roadmap • Knowledge entity summarization • Relation extraction of knowledge entity • Construction of a knowledge base of knowledge entities • Bibliometrics of knowledge entity • Evaluation of knowledge entity in the scientific documents • Application of knowledge entity extraction


  • Chengzhi Zhang

    Chengzhi Zhang ( is a professor of Department of Information Management, Nanjing University of Science and Technology, China.

  • Philipp Mayr

    Philipp Mayr ( is a team leader at the GESIS - Leibniz-Institute for the Social Sciences department Knowledge Technologies for the Social Sciences (WTS).

  • Wei Lu Wei Lu  &

    Wei Lu

    Wei Lu ( is a professor of School of Information Management and director of Information Retrieval and Knowledge Mining Center, Wuhan University.

  • Yi Zhang

    Yi Zhang ( works as a Senior Lecturer at the Australian Artificial Intelligence Institute, University of Technology Sydney.

Articles (9 in this collection)