{"title":"知识图谱中不确定地理空间信息的表示","authors":"L. Cadorel, A. Tettamanzi, Fabien L. Gandon","doi":"10.1145/3557990.3567588","DOIUrl":null,"url":null,"abstract":"This paper highlights the challenges of representing uncertain geospatial information in knowledge graphs. We propose to use Real Estate advertisements since professionals use a lot of vernacular and vague places in order to promote a house to their target audience. Then, we suggest to model local place names using fuzzy set theory. Finally, we discuss how to build a knowledge graph that represents extracted geospatial objects and their uncertainty.","PeriodicalId":117618,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Towards a representation of uncertain geospatial information in knowledge graphs\",\"authors\":\"L. Cadorel, A. Tettamanzi, Fabien L. Gandon\",\"doi\":\"10.1145/3557990.3567588\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper highlights the challenges of representing uncertain geospatial information in knowledge graphs. We propose to use Real Estate advertisements since professionals use a lot of vernacular and vague places in order to promote a house to their target audience. Then, we suggest to model local place names using fuzzy set theory. Finally, we discuss how to build a knowledge graph that represents extracted geospatial objects and their uncertainty.\",\"PeriodicalId\":117618,\"journal\":{\"name\":\"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3557990.3567588\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3557990.3567588","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a representation of uncertain geospatial information in knowledge graphs
This paper highlights the challenges of representing uncertain geospatial information in knowledge graphs. We propose to use Real Estate advertisements since professionals use a lot of vernacular and vague places in order to promote a house to their target audience. Then, we suggest to model local place names using fuzzy set theory. Finally, we discuss how to build a knowledge graph that represents extracted geospatial objects and their uncertainty.