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<p>Enhancing research efficiency by managing related knowledge and forming standardized applications has always been an effort to optimize the work of off-site research institutes, and knowledge sharing is one of the most important methods in many practices. In this paper, the entity matching algorithm is used to identify and match the nodes in the knowledge graph, the connection algorithm is used to calculate and sort the data volume of each node, and all the nodes in the knowledge graph are connected in series. After introducing the update mechanism to establish the decentralized global knowledge graph, the knowledge graph has been integrated into the knowledge-sharing system to enable knowledge sharing between off-site research institutes. In this paper, the accuracy of the full-text search for knowledge graphs can reach 87%, which meets the demand for knowledge sharing. The speed of researchers’ research plan formulation in heterogeneous research institutes has been accelerated by 85.40%, and the efficiency of the research institutes has significantly improved. At the same time, the institute’s innovation ability also increased significantly, with a relative increase of 57.21% in the average number of patents filed annually. This paper provides a reference path for the realization of knowledge sharing among heterogeneous research institutes. It establishes a foundation for significant improvements in the efficiency and innovation ability of research institutes.</p>
</abstract>

Enhancing research efficiency by managing related knowledge and forming standardized applications has always been an effort to optimize the work of off-site research institutes, and knowledge sharing is one of the most important methods in many practices. In this paper, the entity matching algorithm is used to identify and match the nodes in the knowledge graph, the connection algorithm is used to calculate and sort the data volume of each node, and all the nodes in the knowledge graph are connected in series. After introducing the update mechanism to establish the decentralized global knowledge graph, the knowledge graph has been integrated into the knowledge-sharing system to enable knowledge sharing between off-site research institutes. In this paper, the accuracy of the full-text search for knowledge graphs can reach 87%, which meets the demand for knowledge sharing. The speed of researchers’ research plan formulation in heterogeneous research institutes has been accelerated by 85.40%, and the efficiency of the research institutes has significantly improved. At the same time, the institute’s innovation ability also increased significantly, with a relative increase of 57.21% in the average number of patents filed annually. This paper provides a reference path for the realization of knowledge sharing among heterogeneous research institutes. It establishes a foundation for significant improvements in the efficiency and innovation ability of research institutes.

Research on Knowledge Sharing and Innovation Capacity Enhancement of Heterogeneous Research Institutes under the Construction of Knowledge Mapping

Suzhou Institute of North China Electric Power University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Enhancing research efficiency by managing related knowledge and forming standardized applications has always been an effort to optimize the work of off-site...

Research on Knowledge Sharing and Innovation Capacity Enhancement of Heterogeneous Research Institutes under the Construction of Knowledge Mapping

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1460

Schlüsselwörter
Knowledge graph, Entity matching, Connectivity algorithm, Knowledge sharing, Cross-location research institutes

© 2024 Zhilei Hua et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Knowledge Sharing and Innovation Capacity Enhancement of Heterogeneous Research Institutes under the Construction of Knowledge Mapping

Online veröffentlicht: 10. Juni 2024

Seitenbereich: -

Eingereicht: 10. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1460

SchlüsselwörterKnowledge graph, Entity matching, Connectivity algorithm, Knowledge sharing, Cross-location research institutes

© 2024 Zhilei Hua et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Knowledge graph, Entity matching, Connectivity algorithm, Knowledge sharing, Cross-location research institutes