Design and Implementation of a Platform for Domain Knowledge Graph Construction and Applications

doi:10.11871/jfdc.issn.2096-742X.2023.03.008

Abstract

Abstract:

[Objective] The entities, concepts, relationships, etc. of the objective world can be abstracted into a graph data structure by knowledge graph technology. It is a research hotspot of modeling and applications in the vertical domain. In this paper, an automated and integrated platform is developed and implemented, which can cover the whole process from resource scheduling and knowledge graph construction to applications. [Methods] The data processing module, algorithm warehouse, and knowledge graph common components are deployed in the form of microservice architecture. In order to improve the cooperation ability between multiple modules, the platform adopts a low-coupling, high-cohesion, and multi-layered architecture to handle different tasks such as knowledge extraction, storage, and applications. [Results] The knowledge graph construction and application platform in the vertical domain are built and deployed. It provides functions such as data processing, automatic knowledge extraction, graph computation and extension, graph representation learning and application. The usability of the platform is verified, with the construction of a knowledge graph and application of representation learning as a typical case in the financial domain. [Conclusions] The platform covers the whole process from graph construction, graph representation learning to domain applications for knowledge graphs in the vertical domain. It effectively reduces the difficulty of use, improves the efficiency of resource utilization, and can be extended to other vertical domains.

Key words: Knowledge graph, Resource virtualization, Graph representation learning, Microservices technology

ZHANG Boyao, CAO Rongqiang, WAN Meng, SUN Jingqi, WANG Yangang, WANG Jue, ZHAO Yonghua. Design and Implementation of a Platform for Domain Knowledge Graph Construction and Applications[J]. Frontiers of Data and Computing, 2023, 5(3): 111-122, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.03.008.

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