A Research Of Semantic Retrieval Based On Academic Knowledge Graph

With the rapid development of big data,the ever-increasing information resources have enabled various fields to begin a quantitative process.At the same time,with the explosive growth and spread of knowledge,academic information resources are rapidly expanding at an unprecedented speed,and many excellent academic resource databases have been born,and academic research has burst into new vitality.However,as the access to academic resources has gradually increased and the cost of access has gradually decreased,the demand for academic resources by researchers has gradually shifted from the acquisition of resources and knowledge storage carriers to the acquisition of knowledge contained in resources.The process of demand transformation from resource breadth to resource depth is reflected in the fact that its retrieval requirements for academic resources are no longer limited to the level of text resources,but are refined to more specific resources such as research methods,research institutions,research results and research data.level.However,the search methods used by commonly used academic resource databases are still based on keyword-based matching and text-based content matching,which cannot fully express semantic information,and it is difficult to meet the research users' demand for academic resources.Therefore,this paper studies the semantic retrieval based on the academic knowledge graph.With the help of the powerful semantic relation network of the knowledge graph,a semantic retrieval model based on the academic knowledge graph is constructed to realize the semantic retrieval of the academic resources.At the same time,the search performance of the search engine is improved,so that the understanding of the search sentences of scientific research users can be improved from matching at the keyword level to matching at the semantic level.Retrieval effect.The main research sequence of this paper is as follows:First,on the basis of fully investigating the existing large-scale academic knowledge graph,the problem of higher threshold for building large-scale domain knowledge graph is combined with the demand for academic knowledge graph in actual scientific research Mainly lies in the organizational structure of knowledge rather than the size of the map.A method for rebuilding the knowledge map based on the developed large-scale knowledge map,by setting extraction rules to obtain its knowledge base subset data,And finally constructed an experimental small-scale academic knowledge map,and completed the storage of knowledge map data through Neo4j;then,a semantic retrieval model based on the academic knowledge map was constructed,and its main components include storage and index module,and retrieval sentence interaction Modules and semantic analysis modules,and research and analysis of the technical theories and algorithm programs involved in these modules,including index construction,natural language word segmentation processing and synonymous conversion,concept mapping,semantic expansion and semantic reasoning;finally,completed in Neo4j Based on knowledge graph storage,use Lucene builds an entity index,calls Cypher statements to complete database queries,and completes related algorithms in the semantic retrieval model through Python,and implements the retrieval function of the semantic retrieval model based on the academic knowledge graph.In the empirical evaluation link,multiple sets of retrievals are set up The words were tested,and the retrieval performance of the model was evaluated and analyzed based on the retrieval performance evaluation indexes of precision rate,recall rate and F1 value,and compared with the traditional retrieval method based on keyword matching.Through empirical evaluation,it is found that the application of academic knowledge graph to the semantic retrieval of academic resources has certain advantages in retrieval performance such as recall rate and precision rate compared with the traditional keyword-based retrieval method.The semantic retrieval model based on the academic knowledge graph not only supports the structured organization of academic resources,making its internal knowledge more semantic,but also provides domain knowledge data with inherent semantic correlation for semantic retrieval,thereby improving the retrieval system for user retrieval.The semantic comprehension ability of the sentence,and output the results with high semantic correlation between the retrieval data set and the retrieval conditions in a structured form,to provide users with academic resources that better meet their semantic needs.