Design And Implementation Of Intelligent Medical Question Answering System Based On Knowledge Graph

With the rapid development of Internet technology,the information acquisition method based on traditional search engine technology has been difficult to meet people’s needs for knowledge acquisition.Users still need to take extra time to filter the searched web links to ensure the most satisfactory.Information.Therefore,it is always people’s desire to be able to obtain useful information quickly and accurately.The emergence of intelligent question answering system makes up for this shortcoming,it can understand and analyze the user’s intention through the user’s natural language questions,and then give the most direct answer.On the other hand,the technical problems related to knowledge graphs are an indispensable part of the direction of artificial intelligence.As a source of high-quality answers,it can provide accurate data support for question and answer services and is widely used in intelligent question answering systems.Since the system of my country’s medical health service platform is not mature enough,in the actual investigation activities,it is found that people’s demand for knowledge acquisition in the field of medical health is very large.In this paper,the medical field is selected as an example for research.Under the above background,this paper designs and implements a medical wisdom question answering system based on knowledge graph to provide users with high-quality knowledge question answering service.In the process of designing and implementing the intelligent question answering system based on knowledge graph,this paper mainly focuses on the construction of knowledge graph and the application of intelligent question answering.The main research directions of this article include the following points:(1)Research on how to build a knowledge graph in the field of medical and health.The main steps include: knowledge extraction,fusion and storage.Among them,knowledge extraction uses different extraction methods for different data sources,mainly using web crawler technology,named entity recognition model based on Bi-LSTM-CRF and other related technologies to achieve entity extraction,and add attention to the two-way LSTM The attention mechanism realizes the relationship extraction,adopts the entity alignment method of entity relationship attribute similarity to realize knowledge fusion,and uses the graphic database Neo4 j to store the extracted entity attribute relationship and other elements.(2)Study how to complete the question and answer in the field of medical intelligence based on knowledge graph.It mainly includes two parts:semantic search and intelligent question answering.The SVM-based classifier is used to complete the classification of the statement of the user’s intention.The cosine similarity matching can calculate the similarity between entities and return the most similar results.The Stanford corenlp tool performs dependency syntax analysis on the questions to obtain the intention of the user’s question,uses the query language Cypher of the knowledge graph database Neo4 j to perform keyword matching search,and finally uses the We Chat public account platform as an interactive interface to build a collection of medical A platform for intelligent question answering services for health consultation services and chatting functions.Based on the research of the above algorithms and models,this paper completed a knowledge question-answering system based on knowledge graphs in the medical and health field.Through relevant experimental tests and results analysis,the relevant test conditions of this system are good,can be achieved and meet the actual needs of users.The function of medical and health-related consultation can quickly and accurately answer the natural language questions of users,and can bring a good sense of practical experience to users.Therefore,the medical wisdom question answering system based on knowledge graph designed in this paper has practical significance and value.