{"title":"时空RDF数据的路径近似匹配","authors":"Jiajia Lu, Xiaofeng Di, Luyi Bai","doi":"10.1109/IRI49571.2020.00032","DOIUrl":null,"url":null,"abstract":"Due to an ever-increasing number of RDF data with time features and space features, it is an important task to query efficiently spatiotemporal RDF data over RDF datasets. In this paper, the spatiotemporal RDF data contains time features, space features and text features, which are processed separately to facilitate query. Meanwhile the decomposition graph algorithm and the combination query paths algorithm are designed. The query graph with spatiotemporal features is split into multiple paths, and then every path in the query graph is used to search for the best matching path in the path sets contained in the data graph. Due to the existence of inaccurate matchings, approximate matchings are performed according to the evaluation function to find the best matching path. Finally, all the best paths are combined to generate a matching result graph. Our approach is evaluated from approximate performances and query performances. The experimental results show that the effectiveness and efficiency of our method","PeriodicalId":93159,"journal":{"name":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","volume":"61 1575 1","pages":"172-179"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Approximate Matching of Spatiotemporal RDF Data by Path\",\"authors\":\"Jiajia Lu, Xiaofeng Di, Luyi Bai\",\"doi\":\"10.1109/IRI49571.2020.00032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to an ever-increasing number of RDF data with time features and space features, it is an important task to query efficiently spatiotemporal RDF data over RDF datasets. In this paper, the spatiotemporal RDF data contains time features, space features and text features, which are processed separately to facilitate query. Meanwhile the decomposition graph algorithm and the combination query paths algorithm are designed. The query graph with spatiotemporal features is split into multiple paths, and then every path in the query graph is used to search for the best matching path in the path sets contained in the data graph. Due to the existence of inaccurate matchings, approximate matchings are performed according to the evaluation function to find the best matching path. Finally, all the best paths are combined to generate a matching result graph. Our approach is evaluated from approximate performances and query performances. The experimental results show that the effectiveness and efficiency of our method\",\"PeriodicalId\":93159,\"journal\":{\"name\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"volume\":\"61 1575 1\",\"pages\":\"172-179\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRI49571.2020.00032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science : IRI 2020 : proceedings : virtual conference, 11-13 August 2020. IEEE International Conference on Information Reuse and Integration (21st : 2...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRI49571.2020.00032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Approximate Matching of Spatiotemporal RDF Data by Path
Due to an ever-increasing number of RDF data with time features and space features, it is an important task to query efficiently spatiotemporal RDF data over RDF datasets. In this paper, the spatiotemporal RDF data contains time features, space features and text features, which are processed separately to facilitate query. Meanwhile the decomposition graph algorithm and the combination query paths algorithm are designed. The query graph with spatiotemporal features is split into multiple paths, and then every path in the query graph is used to search for the best matching path in the path sets contained in the data graph. Due to the existence of inaccurate matchings, approximate matchings are performed according to the evaluation function to find the best matching path. Finally, all the best paths are combined to generate a matching result graph. Our approach is evaluated from approximate performances and query performances. The experimental results show that the effectiveness and efficiency of our method