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Title: Connecting knowledge spaces: Enabling cross‐community knowledge building through boundary objects
NSF-PAR ID:
10117641
Author(s) / Creator(s):
;
Publisher / Repository:
Wiley-Blackwell
Date Published:
Journal Name:
British Journal of Educational Technology
Volume:
50
Issue:
5
ISSN:
0007-1013
Page Range / eLocation ID:
p. 2144-2161
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  2. Purpose

    This study aims to evaluate a method of building a biomedical knowledge graph (KG).

    Design/methodology/approach

    This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

    Findings

    With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

    Originality/value

    The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

     
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