Algorithm Design of Dynamic Question Answering System Based on Knowledge Base

Jiang, Jingjing; Wang, JiaShun; Wang, Dandan; Xue, Sichuang

doi:10.1007/978-3-030-43306-2_43

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…With the deepening of research, knowledge mapping has been applied to medical treatment [1][2] , chemical industry 3 , electric power [4][5][6][7][8][9] and other fields. Knowledge mapping has played an important role in the following applications: intelligent search [10][11][12][13][14] , intelligent question and answer [15][16][17] , recommendation system 18 and so on.…”

Section: Introductionmentioning

confidence: 99%

Construction and query of power equipment knowledge map based on graph database

xuan,

Zheng,

Xia

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Power equipment knowledge base is an important part of the digital transformation of power grid equipment management, but the structure of power equipment knowledge is complex, and its operation and maintenance involves equipment technical standards, general systems, product information, equipment encyclopedia and so on. In order to improve the utilization rate of data and mine the related information between devices, it needs to be processed and stored uniformly. In view of the above content, this paper introduces a construction and query method of power equipment knowledge map based on graph database. With the help of knowledge map technology, the text data of power equipment is processed, and the query method of power equipment knowledge is specified to complete the construction and query of power device knowledge map based on graph database. It realizes the standardized storage of power equipment knowledge, greatly improves the operation performance of complex associated data, and realizes the rapid query of power equipment technical standards, general systems, product information, equipment encyclopedia and other information. Experiments show that the model has a better recognition effect for entities with more times and stronger regularity, and achieves a more efficient and convenient effect in practical application.

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