Zhang Jing (张 静), Ma Chune (马春娥), Zhao Chenting (赵晨婷), Zhang Jun (张 军), Yi Li (易 力), Mao Xinsheng (毛新生)
{"title":"一种新的关系数据库关联数据排序框架","authors":"Zhang Jing (张 静), Ma Chune (马春娥), Zhao Chenting (赵晨婷), Zhang Jun (张 军), Yi Li (易 力), Mao Xinsheng (毛新生)","doi":"10.1016/S1007-0214(10)70111-5","DOIUrl":null,"url":null,"abstract":"<div><p>This paper investigates the problem of ranking linked data from relational databases using a ranking framework. The core idea is to group relationships by their types, then rank the types, and finally rank the instances attached to each type. The ranking criteria for each step considers the mapping rules and heterogeneous graph structure of the data web. Tests based on a social network dataset show that the linked data ranking is effective and easier for people to understand. This approach benefits from utilizing relationships deduced from mapping rules based on table schemas and distinguishing the relationship types, which results in better ranking and visualization of the linked data.</p></div>","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1007-0214(10)70111-5","citationCount":"3","resultStr":"{\"title\":\"A Novel Ranking Framework for Linked Data from Relational Databases\",\"authors\":\"Zhang Jing (张 静), Ma Chune (马春娥), Zhao Chenting (赵晨婷), Zhang Jun (张 军), Yi Li (易 力), Mao Xinsheng (毛新生)\",\"doi\":\"10.1016/S1007-0214(10)70111-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper investigates the problem of ranking linked data from relational databases using a ranking framework. The core idea is to group relationships by their types, then rank the types, and finally rank the instances attached to each type. The ranking criteria for each step considers the mapping rules and heterogeneous graph structure of the data web. Tests based on a social network dataset show that the linked data ranking is effective and easier for people to understand. This approach benefits from utilizing relationships deduced from mapping rules based on table schemas and distinguishing the relationship types, which results in better ranking and visualization of the linked data.</p></div>\",\"PeriodicalId\":60306,\"journal\":{\"name\":\"Tsinghua Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/S1007-0214(10)70111-5\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tsinghua Science and Technology\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1007021410701115\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tsinghua Science and Technology","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1007021410701115","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A Novel Ranking Framework for Linked Data from Relational Databases
This paper investigates the problem of ranking linked data from relational databases using a ranking framework. The core idea is to group relationships by their types, then rank the types, and finally rank the instances attached to each type. The ranking criteria for each step considers the mapping rules and heterogeneous graph structure of the data web. Tests based on a social network dataset show that the linked data ranking is effective and easier for people to understand. This approach benefits from utilizing relationships deduced from mapping rules based on table schemas and distinguishing the relationship types, which results in better ranking and visualization of the linked data.