Extraction and Evaluation of Knowledge Entities from Scientific Documents

Abstract

As a core resource of scientific knowledge, academic documents have been frequently used by scholars, especially newcomers to a given field. In the era of big data, scientific documents such as academic articles, patents, technical reports, and webpages are booming. The rapid daily growth of scientific documents indicates that a large amount of knowledge is proposed, improved, and used (Zhang et al., 2021). In scientific documents, knowledge entities (KEs) refer to the knowledge mentioned or cited by authors, such as algorithms, models, theories, datasets and software, diseases, drugs, and genes, reflecting rich resources in diverse problemsolving scenarios (Brack et al., 2020; Ding et al., 2013; Hou et al., 2019; Li et al. 2020). The advancement, improvement, and application of KEs in academic research have played a crucial role in promoting the development of different disciplines. Extracting various KEs from scientific documents can determine whether such KEs are emerging or typical in a specific field, and help scholars gain a comprehensive understanding of these KEs and even the entire research field (Wang & Zhang, 2020). KE extraction is also useful for multiple downstream tasks in information extraction, text mining, natural language processing, information retrieval, digital library research, and so on (Zhang et al., 2021). Particularly for researchers in artificial intelligence (AI), information science, and other related disciplines, discovering methods from large-scale academic literature, and evaluating their performance and influence have become increasingly necessary and meaningful (Hou et al., 2020). There are four kinds of methods of KE extraction in scientific documents. They are manual annotation-based (Chu & Ke, 2017; Tateisi et al., 2014; Zadeh & Schumann, 2016), rule-based (Kondo et al., 2009), statistics-based (Heffernan & Teufel, 2018; Névéol, Wilbur, & Lu, 2011; Okamoto, Shan, & Orihara, 2017), and