Diet And Health Knowledge Question Answering System Based On Knowledge Graph

With the improvement of people’s living standards,people pay more and more attention to health issues,and healthy eating has become a hot spot of people’s attention.At present,most people acquire knowledge about diet and health through popular science articles or short video clips,but often cannot get the answer directly,and need to filter useful information.Therefore,building an effective and practical smart diet health question answering system has become an urgent problem to be solved.In response to this problem,this paper collects and processes massive diet and health data,constructs a nutrition and health knowledge graph,and designs and implements a nutrition and health intelligent question answering system based on knowledge graph and natural language processing technology to solve the knowledge acquisition of dietary health in people’s daily life.question.The main work of this paper is as follows:(1)Build a knowledge graph in the field of diet and health.At present,the opensource Chinese knowledge graph does not have a knowledge graph related to the field of diet and health.This article uses the Scrapy crawler technology to crawl the website data related to diet and health,and then combines with the existing project database data to construct the source data of the knowledge graph.This article uses the Protégéontology.The modeling software designs ontology,and uses the constructed ontology and domain knowledge to build a knowledge map in the field of diet and health.(2)Research and implement the automatic labeling technology of datasets based on domain named entity recognition.By constructing a domain entity dictionary,the dictionary-based bidirectional maximum matching algorithm performs word segmentation for the sentences to be marked,and then performs B-I-E-S-O marking on the sentences through scripts.(3)Using the dietary health knowledge map constructed in this paper as the data support to study the intelligent question answering task of dietary health knowledge.In this paper,the question answering algorithm is divided into two tasks: question attribute linking and named entity recognition.First,the BERT-BiLSTM-CRF-based named entity recognition model is used for the question proposed by the user,and the domain entities in the question are identified and constructed at the same time.Obtain the candidate attribute set corresponding to the question,and then use the BERT-based question attribute link model to determine the corresponding entity attribute of the question,and find the answer in the graph database Neo4 j based on key features and rules.(4)Design and implement a question and answering system for dietary health knowledge.The system adopts Spring,SpringBoot,SpringMVC,and MyBatis architecture.The model end is made into the form of an API interface through the Flask architecture for system calls.The system function modules are divided into four modules: named entity recognition function module,fruit and vegetable display module,entity details module,and intelligent question answering module.Intelligent question answering is the core module,which can intelligently answer the questions raised by users in the field of diet and health.