Design And Implementation Of Domain-Specific Knowledge Sharing System

Under the background of the rapid development of the Internet,the knowledge information in the network has ushered in an explosive growth,and a large amount of domain knowledge is scattered and hidden in the network data.In the face of massive domain knowledge,it becomes very difficult to obtain the key information needed in the first time.Due to the lack of a unified knowledge integration management and visual analysis method,the sharing rate of knowledge is not high,and effective analysis and reuse cannot be carried out.In addition,under the development trend of data diversification,people’s demand for multi-dimensional representation of knowledge is increasing,which puts forward higher requirements for knowledge sharing management and visual analysis.At present,some knowledge platforms can provide knowledge management functions,but they cannot effectively integrate and integrate complex and redundant knowledge,and they are limited to simple display of knowledge and cannot provide rich display forms.In response to the above problems,this paper designs and develops a knowledge sharing visualization system oriented to domain data.The system organizes the complex and discrete information in domain texts into structured knowledge through knowledge extraction,knowledge fusion,knowledge graph construction and other technologies.The knowledge base also visualizes the knowledge graph construction process,realizes the automatic management and application of knowledge,and visualizes the semantic logic and data association of knowledge from multiple dimensions and levels.The domain-specific knowledge sharing system is mainly divided into four modules:knowledge extraction module,knowledge fusion module,knowledge graph construction module,and visual display module:(1)For the discrete domain knowledge implicit in complex and diverse network texts,the knowledge extraction module passes Dependency parsing matches predefined semantic paradigms,extracts unstructured text information from texts and organizes them into structured event tuple forms.(2)In view of the redundancy and conflict problems in the event multi-group,the knowledge fusion module adopts the entity alignment and cross-validation technology based on deep learning to ensure the efficiency and accuracy of knowledge fusion.(3)For massive data sources,the knowledge graph building module adopts a distributed architecture for massive knowledge storage,re-models the data through the attribute graph model,and finally uses the query sentence optimization algorithm to store and query to achieve knowledge sharing management.(4)Aiming at the problem of a single data presentation method in the knowledge sharing system,optimize the system interaction effect through charts,maps,etc.,introduce the map interface to draw element trajectories containing geographic location information,and finally use the hierarchical division algorithm to solve the visualization of knowledge graphs under massive data.Rendering lag problem.After the design and implementation of the system is completed,the system test is carried out according to the functional and non-functional requirements of the system.The functional modules of the knowledge sharing system in specific fields can work normally and achieve the expected results.The system extracts and uniformly expresses domain knowledge through knowledge extraction and fusion technology,and displays domain knowledge from multiple perspectives through a visual display module,realizing automatic management and application of domain knowledge and providing the ability to share knowledge.