Question And Answer Retrieval Method Of Location Information Based On Ontology And Topic Classification

With the rapid development of indoor and outdoor positioning technology and the continuous improvement of mobile storage device processing capacity,location-based services are increasingly appearing in people's daily life.People can easily obtain various location-related life service information they need through mobile terminals.In information retrieval,people tend to choose keywords or natural sentence questions to express their query intention,which requires the retrieval service to be able to quickly and accurately understand the semantic meaning of the user input questions.The natural language questions entered by users vary in length and form,and often contain colloquial expressions,making it difficult to use rules or templates for induction.Aiming at the problem of how to understand the actual query requirements of the user input and retrieve the relevant information,this paper proposes a question and answer retrieval method of location information based on topic classification.After preprocessing the questions entered by users and classifying them into corresponding retrieval topics,extract the valid information in the question according to the retrieval strategy of the topic,then carry out structured semantic retrieval of the location ontology data to obtain the specified entity result set,and based on the information of the retrieval results,the content combining graph and text are displayed to realize the instant response to the question.Firstly,define the logical structure of the location ontology based on the analysis of the connotation of the location information,so as to realize the modeling and storage of semantic location information based on ontology and provide data basis for questionand-answer retrieval service.Secondly,this paper introduces the method of topic categorization based on sample and semantic sequence in detail.This method calculates the similarity between the semantic sequence of the current question and the questions in the sample library rather than the original text according to the N-Gram model,and then gets the topic of the most similar sample to be matched as the result,achieving high accuracy based on a small number of samples.This paper selects the Elasticsearch as a tool to implement the construction of sample library and proximity matching between the current question and samples.Then this paper analyzes the characteristics of ontology query and regards the question entered by user as a combination of retrieval condition set,query item set and concept.According to the custom topic retrieval strategy,extract the effective information about the retrieval condition and query item in the question automatically,generate the query semantic graph and construct the SPARQL query statement to perform the semantic retrieval operation,avoiding the grammar analysis of the complex question.Finally,this paper select the shopping of mall as the application background,and put the question-and-answer retrieval method into practice.First of all,establish the location ontology model based on the characteristics of indoor shopping mall data,secondly sort out the basic lexicon and collect natural language questions after summarizing the question-and-answer retrieval requirements of shopping malls by combining the existing shopping mall service research and user survey,and then divide different topics and design retrieval strategies for each topic.After the application of question and answer retrieval in shopping mall is completed,test and analyze the result.