Research On Place Name Matching And Place Name Translation Method Based On Multiple Data Sources

The research topic of this paper is based on global geographical name matching and translation methods based on multiple data sources.With the advent of the era of global integration,the Belt and Road Initiative is playing an increasingly important role,and the demand for geographic information is also increasing.Integrating different sources and different types of data to form standardized GIS data is an important task in the geographic information industry.In actual work,because the target and technology are different,the data source is different,and the obtained data often needs to be matched by data consistency before it can be used.The increasing demand of users for Toponymic data determines the urgency and necessity of global Toponymic matching and Chinese translation.This paper first elaborates on the theory of geographical names.Based on the background and research significance of the semantic matching of geographical names,this paper reviews the semantic consistency and translation research progress of geographical names and geospatial data,and deeply analyzes the concepts and theories of geographical names and spatial data consistency.The related research on the semantics of place names,the characteristics of place names,the classification of place names and the formation of other place names were carried out.The development trend of place names and the influencing factors of place name renewal were summarized.The database and place names of place names from the translation and management of place names were summarized.The current status of the data is analyzed.Secondly,this paper designs a multi-data source place name semantic matching algorithm.This paper summarizes several toponymic semantic matching algorithms with high usage rate.According to the advantages and disadvantages of the algorithm,the related algorithms are optimized and improved.On the basis of summarizing the characteristics of standardized place names,from the cognitive habits of people to place names,through the calculation of the semantic similarity of geographical names and the semantic consistency of the spatial topological relations of geographical entities,the comprehensive semantic matching of geographical names is processed to improve the accuracy and matching efficiency of geographical names.Finally,using the improved toponymic matching algorithm in this paper,the design of the geographical name semantic matching and translation comprehensive similarity calculation tool set is realized.On the one hand,the similarity calculation of reasonable weight distribution of place names,on the other hand,the introduction of Google geographical names translation,bing dictionary translation,bing translation and Baidu translation four geographical names matching translation network geographical names database.Comprehensive semantic matching and translation of place name data.The matching results data is compared with the National Geographic Information Center's 2007 version of the global toponymic data and the Ministry of Civil Affairs standard place names.The results show that the method used in this paper is effective.This paper designs and implements a global name translation method based on multiple data sources,and develops a set of toponymic translation tools based on multiple data sources.This tool set is used to match global geograph data,translate experiments,and global vector data.Batch processing of geographical names data and standardization of geographical names.It is applied to the National Basic Geographic Information Center's "1 million global vector name translation and boundary data update processing" project.The results show that the use of this method greatly reduces the manual workload of geographical names processing.The translation effect in all regions of the world has been greatly improved compared with the translation results of a single data source.The average sampling rate of the sample reached 91.02%,and the average translation reliability rate of all data statistics reached 87.66%.