Construction and Application of Knowledge Graph for Troubleshooting of High-speed Railway Turnout Equipment
In order to solve the problems of difficult storage and application of a large amount of knowledge generated in the process of turnout equipment fault handling as well as the inability to directly retrieve and reason semi-structured fault text data,a method was proposed to construct a knowledge graph for high-speed rail turnout equipment fault han-dling.Firstly,based on a small amount of manually annotated fault text data,a BERT-BiLSTM-CRF*deep learning model based on reinforcement learning was used to extract entities.Then a relationship-matching template was designed to extract the relationship between entities according to the knowledge domain characteristics and data structure.Subse-quently,for the problem of entity redundancy,a combination of text similarity and structural similarity was used to fuse fault entities.Finally,807 entities were extracted with annotation accuracy rate of over 90％,and 2,232 entity relation-ships were constructed based on the fault texts of high-speed railway turnouts equipment in the past three years from each railway bureau in China.The knowledge graph of high-speed railway turnout equipment fault handling constructed based on the above knowledge visually displays the fault relationships of turnout equipment,realizes the data association queries between different entity objects,explores the implied relationships,assists turnout maintenance personnel to make fault handling decisions and realizes preventive maintenance of turnout equipment to extend equipment life.
NETL
NSTL
万方数据
