{"title":"用 RDF 管理时态数据的调查","authors":"Di Wu , Hsien-Tseng Wang , Abdullah Uz Tansel","doi":"10.1016/j.is.2024.102368","DOIUrl":null,"url":null,"abstract":"<div><p>The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of <span><math><mrow><mo><</mo><mi>s</mi><mi>u</mi><mi>b</mi><mi>j</mi><mi>e</mi><mi>c</mi><mi>t</mi><mspace></mspace><mi>p</mi><mi>r</mi><mi>e</mi><mi>d</mi><mi>i</mi><mi>c</mi><mi>a</mi><mi>t</mi><mi>e</mi><mspace></mspace><mi>o</mi><mi>b</mi><mi>j</mi><mi>e</mi><mi>c</mi><mi>t</mi><mo>></mo></mrow></math></span>. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":"122 ","pages":"Article 102368"},"PeriodicalIF":3.0000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A survey for managing temporal data in RDF\",\"authors\":\"Di Wu , Hsien-Tseng Wang , Abdullah Uz Tansel\",\"doi\":\"10.1016/j.is.2024.102368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of <span><math><mrow><mo><</mo><mi>s</mi><mi>u</mi><mi>b</mi><mi>j</mi><mi>e</mi><mi>c</mi><mi>t</mi><mspace></mspace><mi>p</mi><mi>r</mi><mi>e</mi><mi>d</mi><mi>i</mi><mi>c</mi><mi>a</mi><mi>t</mi><mi>e</mi><mspace></mspace><mi>o</mi><mi>b</mi><mi>j</mi><mi>e</mi><mi>c</mi><mi>t</mi><mo>></mo></mrow></math></span>. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.</p></div>\",\"PeriodicalId\":50363,\"journal\":{\"name\":\"Information Systems\",\"volume\":\"122 \",\"pages\":\"Article 102368\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306437924000267\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437924000267","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of . Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.
期刊介绍：
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.
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