Research On The Construction And Application Of Tourism Knowledge Graph Based On Interactive Attention Network

With the increasing demand for tourism and the development of the tourism industry,the number of related websites has skyrocketed,tourism data is constantly growing,and users are facing the problem of information overload.Traditional search engines only search and return relevant pages based on keywords,and the final results still need to be manually filtered.However,knowledge graph based question answering systems can semantically parse,accurately identify user intentions,and return accurate and concise answers,which effectively improve retrieval efficiency.Therefore,this dissertation aims to construct and apply a tourism knowledge graph that integrates interactive attention networks based on user needs.The main work is as follows:(1)To address the current lack of publicly available tourism datasets on the Internet,a tourism dataset TDDS is constructed.First of all,a total of 20,000 scenic spot data are crawled from some sites using Python;After data cleaning,data fusion is achieved using address attribute similarity matching algorithm and scenic spot name similarity calculation;Finally,a remote supervision based method is used to annotate the data,resulting in a tourism dataset TDDS with a total of 9062 pieces of data for the construction of a knowledge graph.(2)A joint entity and relationship extraction model RSIAN that integrates interactive attention networks is proposed to address the issues of entity redundancy and triplet overlap in current entity and relationship extraction.This model uses interactive attention network to learn higher-order semantic associations at the sentence and relationship levels,which enhances the interaction between sentences and relationships,and assists the model in extracting decisions.Comparative experiments,ablation experiments and overlapping triplet analysis experiments are conducted on three Chinese and English datasets,namely TDDS,NYT and Webnlg.The final experimental results show that RSIAN outperforms all the comparative models in terms of accuracy,recall and F1 value with more stable performance and good generalization ability,which fully demonstrate that it can effectively solve the problems of tourism entity redundancy and relationship overlap.(3)In view of the complexity of tourism data sets,a higher quality triplet is obtained based on the combination of the newly built tourist attraction ontology and the RSIAN model,and then Neo4 j is used for persistence storage,and the question answering system is implemented based on the constructed tourism knowledge map.The system first preprocesses the input questions,and then uses the Bi LSTM-CRF model for named entity recognition.In order to improve the matching degree of the answers,a combination of problem template feature word matching and similarity calculation is used for classification,which can improve the accuracy of classification.Then,a Cypher query statement is generated based on the matching category,and answers are given based on the template.The final answer is displayed through text and graphs,which can effectively improve user satisfaction.