Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Abstract

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.

Exploring Research on the Construction and Application of Knowledge Graphs for Aircraft Fault Diagnosis

“…Knowledge graph technology is an innovative tool that aims to mine entity relationships from vast, fragmented information and present them as structured semantic networks, which provide better methods for knowledge mining, representation, and management in the field of artificial intelligence [9][10][11]. With knowledge graph technology, fault knowledge can be extracted from vast, multi-source, heterogeneous fault documents [12]. By integrating the extracted information into a structured interconnected fault knowledge graph and applying reasoning technology, intelligent fault diagnosis strategies can be generated to guide maintenance engineers to locate the faulty unit quickly and accurately.…”