Extracting and Evaluating of Knowledge Entities

Closes:
This special issue will open for submissions on 30 October 2021

In the era of big data, tremendous amounts of information and data have drastically changed human civilization. The rapid growth of scientific documents  indicates that a large amount of knowledge is proposed, improved, and used (Zhang et. al., 2021). This further raises 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 (Chang & Zheng, 2007). As a crucial medium for knowledge transmission, scientific documents that contain rich knowledge entities attract the attention of scholars (Ding et. al. 2013). In scientific documents, knowledge entities refer to the knowledge mentioned or cited by authors, such as algorithms, models, theories, datasets, and software. They also reflect various resources used by 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 (Defense Advanced Research Projects Agency) has recently launched the Automating Scientific Knowledge Extraction (ASKE) project (https://www.darpa.mil/program/automating-scientific-knowledge-extraction) 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 (Auer et. al., 2007) 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 sciences, specialized in information extraction, text mining, natural language processing, information retrieval 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 welcome submissions to this special issue. Topics covered include (but are not limited to):

  • Extraction knowledge and entity from scientific documents
  • Model and algorithmize entity extraction from scientific documents (Wang & Zhang, 2020)
  • Dataset and metrics mention extraction from scientific documents
  • Software and tool extraction from scientific documents (Boland & Krüger, 2019)
  • Construction of a knowledge entity graph and roadmap (Zha et. al., 2019)
  • 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


Deadline and Submission Details

The submission deadline for all papers is 15 March 2022

The publication date of this special issue is 2022

To submit your research, please visit the ScholarOne manuscript portal. (Note: Select the special issue on ‘Extracting and Evaluating of Knowledge Entities’ please)

To view the author guidelines for this journal, please visit the journal's page.