Introduction
A scientific knowledge graph is a representative domain knowledge graph that focuses on scientific knowledge, takes scholars, periodicals, etc., as resources, and models their entities, attributes, and relations. Scientific knowledge graphs support applications such as technical insight, scholar profiles, and correlation mining of scientific knowledge. However, the normalization of the construction and utilization of scientific knowledge graph has not been perfect. To this end, this guide proposes data governance, construction techniques, integration services, applications, and ecological development for the scientific knowledge graph. It provides guidance for the work of stakeholders such as data suppliers, technology suppliers, integrators, users, and ecological partners of the scientific knowledge graph, and promotes communication and cooperation among stakeholders. The following figure is the framework of scientific knowledge graph defined in this standard.
Overview of the Standard
The purpose of this guide is to assist developers of knowledge graphs in the fields of scientific knowledge to follow a general guideline of data scope, scientific knowledge graph construction process, and applications. With this guide, scientific knowledge graph can be constructed and integrated more efficiently, providing a more complete and accurate knowledge service ecosystem for the scientific industry. This guide enables suppliers to provide compatible knowledge graphs and technologies under a unified knowledge model and interface specification.
The scope of scientific knowledge graph includes the following:
- Data scope, including actors such as authors or organizations; documents such as journal or conference publications; and research knowledge such as research topics or technologies.
- SKG construction process, including knowledge acquisition, knowledge fusion, knowledge representation, or knowledge inference of scientific knowledge.
Applications, including academic service, intelligence mining, or scholar analysis.
Key Features and Benefits
The scientific knowledge graph is a representative domain knowledge graph that focuses on scientific knowledge. The related activities of scientific knowledge graph include providing scientific data, providing scientific knowledge graph construction technology, providing scientific knowledge graph products or services, using scientific knowledge graph, supporting the development of scientific knowledge graph, etc. Relevant stakeholders engaged in these activities are comprised of scientific data suppliers, scientific knowledge graph technology suppliers, scientific knowledge graph integrators, scientific knowledge graph users and scientific knowledge graph ecological partners.
Adoption and Impact
The guide for scientific knowledge graphs provides a unified framework and reference for the construction of knowledge graphs in different scientific fields, facilitating the integration of scientific knowledge. The unification and standardization of the guide enable scientific knowledge graphs to be more widely applied in various scientific domains, expanding their application scenarios and market potential.
Conclusion
The scientific knowledge graph is a representative domain knowledge graph that focuses on scientific knowledge, takes scholars, periodicals, etc., as resources, and models their entities, attributes, and relations. The guide for scientific knowledge graphs proposes data governance, construction techniques, integration services, applications, and ecological development for the scientific knowledge graph. Stakeholders could carry out relevant activities by referring to this guide. This guide provides a unified framework and reference for the construction of knowledge graphs in different scientific fields, facilitating the integration of interdisciplinary knowledge, and enabling scientific knowledge graphs to be more widely applied in various scientific domains.
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Disclaimer: The authors are completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
