Guest editorial: Extraction and evaluation of knowledge entities in the age of artificial intelligence
| Date | 19 June 2023 |
| Pages | 433-437 |
| DOI | https://doi.org/10.1108/AJIM-05-2023-507 |
| Published date | 19 June 2023 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Chengzhi Zhang,Philipp Mayr,Wei Lu,Yi Zhang |
Guest editorial: Extraction and
evaluation of knowledge entities
in the age of artificial intelligence
1. Introduction
Scientific documents serve as an essential mediator for research achievements and scientific
knowledge. With the increasing availability of full-text data and advanced data analytical
techniques, bibliometric researchers have started to focus on granular content within
scientific documents, transitioning their foci from the external metadata of these documents
to the internal knowledge they encompass. In scientific documents, knowledge consists of
many interconnected units, known as knowledge entities (Ding et al., 2013).
Ding et al. (2013) classified knowledge entities into macro-level (e.g. author, journal,
references), meso-level (e.g. keywords) and micro-level (e.g. dataset, method, biomedical
entities) categories. These categories refer to traditional bibliographic entities for evaluation
purposes and knowledge entities found within the full-text content, respectively. The
extraction and evaluation of knowledge entities can improve existing knowledge services
and meet the needs of researchers for rapid and accurate access to scientific knowledge (Ma
et al., 2023;Mayr et al., 2014). Scholars can evaluate the scientific, social and economic
attributes of knowledge and assess its role in science, technology, innovation and policy.
Analyzing dynamic changes over time and identifying geographical differences can provide
insights to understand the patterns of knowledge use and dissemination, which can promote
knowledge discovery and generate new scientific activities.
The 2nd Workshop on the Extraction and Evaluation of Knowledge Entities from
Scientific Documents (EEKE2021) was held online, co-located with the ACM/IEEE Joint
Conference on Digital Libraries 2021 (JCDL2021) on September 30, 2021. The EEKE
workshop series (https://eekeworkshop.github.io/) aims to engage relevant communities in
addressing open problems in the extraction and evaluation of knowledge entities from
scientific documents. Parti cipants have contributed tremen dous accomplishments in
identifying knowledge entities, exploring entity features, analyzing entity relationships,
and developing extraction platforms or knowledge bases. The workshop has provided
scholars, especially early career researchers, with knowledge recommendations and other
knowledge entity-based services (Zhang et al., 2021).
2. Topics in this special issue
This special issue collected articles presented at the EEKE2021 workshop and external
submissions. A total of 18 papers were submitted, of which 9 were accepted after a rigorous
review process. These 9 articles, contributed by 36 authors from 4 countries (China, USA,
South Korea and Thailand), are included in this collection. In this editorial, we provide an
overview of these papers and introduce the themes and contributions of this Special Issue.
The papers are grouped into three topics: entity extraction and entity relations extraction
Guest editorial
433
Chengzhi Zhang acknowledges the National Natural Science Foundation of China (Grant No. 72074113),
and Yi Zhang acknowledges the Discovery Early Career Researcher Award granted by the Australian
Research Council (Grant No. DE190100994).
Aslib Journal of Information
Management
Vol. 75 No. 3, 2023
pp. 433-437
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-05-2023-507
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