{"title":"评估语义映射","authors":"D. Wardani","doi":"10.11591/EECSI.V5I5.1648","DOIUrl":null,"url":null,"abstract":"Along the increasing of the importance of links in the network of data, they should be considered more in the mapping relational to graph model. Semantic abstraction gaps often occur during the mapping process where the link in the real world is mapped as a node in a graph model. This paper focused on evaluating the result of mapping and converting without losing the semantics. We propose the evaluation of our approach by using schema.org as the semantic standard. The experiments in three data sets show that the semantic mapping approach is pretty effective. We obtain quite good score matching without considering the gap index (the average is 0.6922) and with considering the gap index (the average is 0.5264) and the average precision score, 0.7042, is pretty good too.","PeriodicalId":20498,"journal":{"name":"Proceeding of the Electrical Engineering Computer Science and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating The Semantic Mapping\",\"authors\":\"D. Wardani\",\"doi\":\"10.11591/EECSI.V5I5.1648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Along the increasing of the importance of links in the network of data, they should be considered more in the mapping relational to graph model. Semantic abstraction gaps often occur during the mapping process where the link in the real world is mapped as a node in a graph model. This paper focused on evaluating the result of mapping and converting without losing the semantics. We propose the evaluation of our approach by using schema.org as the semantic standard. The experiments in three data sets show that the semantic mapping approach is pretty effective. We obtain quite good score matching without considering the gap index (the average is 0.6922) and with considering the gap index (the average is 0.5264) and the average precision score, 0.7042, is pretty good too.\",\"PeriodicalId\":20498,\"journal\":{\"name\":\"Proceeding of the Electrical Engineering Computer Science and Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeding of the Electrical Engineering Computer Science and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/EECSI.V5I5.1648\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding of the Electrical Engineering Computer Science and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/EECSI.V5I5.1648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Along the increasing of the importance of links in the network of data, they should be considered more in the mapping relational to graph model. Semantic abstraction gaps often occur during the mapping process where the link in the real world is mapped as a node in a graph model. This paper focused on evaluating the result of mapping and converting without losing the semantics. We propose the evaluation of our approach by using schema.org as the semantic standard. The experiments in three data sets show that the semantic mapping approach is pretty effective. We obtain quite good score matching without considering the gap index (the average is 0.6922) and with considering the gap index (the average is 0.5264) and the average precision score, 0.7042, is pretty good too.