I. Fundulaki, G. Flouris, Vassilis Papakonstantinou
{"title":"On the Use of Abstract Models for RDF/S Provenance","authors":"I. Fundulaki, G. Flouris, Vassilis Papakonstantinou","doi":"10.1201/b16859-21","DOIUrl":"https://doi.org/10.1201/b16859-21","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123507512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing availability of structured data on the Web stimulated a renewed interest in its graph nature. Applications like the Google Knowledge Graph (KG) [227] and the Facebook Graph (FG) [192] build large graphs of entities (e.g., people, places) and their semantic relations (e.g., born in, located in). The KG, by matching keywords in a search request against entities in the graph, enhances Google’s results with structured data in the same spirit of Wikipedia info boxes. The FG by looking at semantic relations between Facebook entities enables searching within this huge social graph. However, both approaches adopt proprietary architectures with idiosyncratic data models and limited support in terms of APIs to access their data and querying capabilities. A precursor of these applications is the Linked Open Data project [275] (see Section 1.6 in Chapter 1). The openness of data and the ground on Web technologies are among the driving forces of Linked Open Data. There is an active community of developers that build applications and APIs both to convert and consume linked data in the Resource Description Framework (RDF) standard data format. These initiatives, which maintain structured information at each node in the Web graph and semantic links between nodes, are making the Web evolve toward a Web of Data (WoD). Fig. 11.1 provides a pictorial representation of the traditional Web and the WoD by looking at the latter from the Linked Open Data perspective. Although sharing a graph-like nature, there are some
Web上结构化数据可用性的增加激发了人们对其图形特性的兴趣。b谷歌Knowledge Graph (KG)[227]和Facebook Graph (FG)[192]等应用程序构建了实体(例如，人，地点)及其语义关系(例如，出生在，位于)的大型图。KG通过将搜索请求中的关键字与图中的实体进行匹配，以与维基百科信息框相同的精神，用结构化数据增强谷歌的结果。通过观察Facebook实体之间的语义关系，FG可以在这个庞大的社交图谱中进行搜索。然而，这两种方法都采用具有特殊数据模型的专有架构，并且在访问数据和查询功能的api方面支持有限。这些应用的先驱是关联开放数据项目[275](见第1章1.6节)。数据的开放性和基于Web技术的基础是关联开放数据的驱动力之一。有一个活跃的开发人员社区，他们构建应用程序和api来转换和使用资源描述框架(RDF)标准数据格式的链接数据。这些在Web图的每个节点上维护结构化信息和节点之间的语义链接的举措，正在使Web向数据Web (Web of Data, WoD)发展。图11.1从关联开放数据的角度对传统Web和世界进行了图示。虽然共享类似图形的性质，但有一些
{"title":"Semantic Navigation on the Web of Data","authors":"Valeria Fionda, Claudio Gutiérrez, G. Pirrò","doi":"10.1201/b16859-16","DOIUrl":"https://doi.org/10.1201/b16859-16","url":null,"abstract":"The increasing availability of structured data on the Web stimulated a renewed interest in its graph nature. Applications like the Google Knowledge Graph (KG) [227] and the Facebook Graph (FG) [192] build large graphs of entities (e.g., people, places) and their semantic relations (e.g., born in, located in). The KG, by matching keywords in a search request against entities in the graph, enhances Google’s results with structured data in the same spirit of Wikipedia info boxes. The FG by looking at semantic relations between Facebook entities enables searching within this huge social graph. However, both approaches adopt proprietary architectures with idiosyncratic data models and limited support in terms of APIs to access their data and querying capabilities. A precursor of these applications is the Linked Open Data project [275] (see Section 1.6 in Chapter 1). The openness of data and the ground on Web technologies are among the driving forces of Linked Open Data. There is an active community of developers that build applications and APIs both to convert and consume linked data in the Resource Description Framework (RDF) standard data format. These initiatives, which maintain structured information at each node in the Web graph and semantic links between nodes, are making the Web evolve toward a Web of Data (WoD). Fig. 11.1 provides a pictorial representation of the traditional Web and the WoD by looking at the latter from the Linked Open Data perspective. Although sharing a graph-like nature, there are some","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133973746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Linked Data Query Processing Based on Link Traversal","authors":"O. Hartig","doi":"10.1201/b16859-15","DOIUrl":"https://doi.org/10.1201/b16859-15","url":null,"abstract":"8.","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121395573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Virtuoso Column Store [185] introduces vectorized execution into the Virtuoso DBMS. Additionally, its scale-out version, that allows running the system on a cluster, has been significantly redesigned. This article discusses advances in scale-out support in Virtuoso and analyzes this on the Berlin SPARQL Benchmark (BSBM) [101]. To demonstrate the features of Virtuoso Cluster RDF Column Store, we first present micro-benchmarks on a small 2node cluster with 10 billion triples. In the full evaluation we show one can now scale-out to a BSBM database of 150 billion triples. The latter experiment is a 750 times increase over the previous largest BSBM report, and for the first time includes both its Explore and Business Intelligence workloads. The storage scheme used by Virtuoso for storing RDF Subject-PropertyObject triples pertaining to a Graph (hence we have quads, not triples) consists of five indexes: PSOG, POSG, SP, OP, GS. To be precise, PSOG is a B-tree with key (P,S,O,G), where P is a number identifying a property, S a subject, O an object and G the graph. Additionally, there is a B-tree holding URIs and a B-tree holding string literals, both of them used to encode string(-URI)s into numerical identifiers. Users may alter the indexing scheme of Virtuoso but this almost never happens. The three last indexes (SP, OP, GS) are projections of the first two covering indexes, containing only the unique combinations – hence these are much smaller. We note that Virtuoso Column Store Edition (V7) departs from the previous Virtuoso editions (V6) in that
{"title":"Experiences with Virtuoso Cluster RDF Column Store","authors":"P. Boncz, O. Erling, M. Pham","doi":"10.1201/b16859-13","DOIUrl":"https://doi.org/10.1201/b16859-13","url":null,"abstract":"Virtuoso Column Store [185] introduces vectorized execution into the Virtuoso DBMS. Additionally, its scale-out version, that allows running the system on a cluster, has been significantly redesigned. This article discusses advances in scale-out support in Virtuoso and analyzes this on the Berlin SPARQL Benchmark (BSBM) [101]. To demonstrate the features of Virtuoso Cluster RDF Column Store, we first present micro-benchmarks on a small 2node cluster with 10 billion triples. In the full evaluation we show one can now scale-out to a BSBM database of 150 billion triples. The latter experiment is a 750 times increase over the previous largest BSBM report, and for the first time includes both its Explore and Business Intelligence workloads. The storage scheme used by Virtuoso for storing RDF Subject-PropertyObject triples pertaining to a Graph (hence we have quads, not triples) consists of five indexes: PSOG, POSG, SP, OP, GS. To be precise, PSOG is a B-tree with key (P,S,O,G), where P is a number identifying a property, S a subject, O an object and G the graph. Additionally, there is a B-tree holding URIs and a B-tree holding string literals, both of them used to encode string(-URI)s into numerical identifiers. Users may alter the indexing scheme of Virtuoso but this almost never happens. The three last indexes (SP, OP, GS) are projections of the first two covering indexes, containing only the unique combinations – hence these are much smaller. We note that Virtuoso Column Store Edition (V7) departs from the previous Virtuoso editions (V6) in that","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132620733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The introduction of stream processing methods in the Semantic Web enables the management of data streams on the Web. Chapter 6 introduced models for RDF stream and several extensions of SPARQL engines with windows for stream processing. The chapter assumes the absence of a TBox, so it is possible to compute the query answer without considering the ontology entailment defined through a TBox described in an ontological language. In this chapter, we relax this constraint and we consider the case of query answering over RDF streams when the TBox is not empty. In particular, we focus on Stream Reasoning [544], the topic that studies how to compute and incrementally maintain the ontological entailments in RDF streams. In traditional Semantic Web reasoning data are usually static or quasistatic1, so the whole computation of the ontological entailment can be executed every time the data change. When we consider RDF streams the static hypothesis is not valid anymore: RDF stream engines work with highly dynamic data and they need to process them faster than new data arrives to avoid congestion states. In this scenario, traditional materialization techniques could fail; a possible solution is the incremental maintenance of the materialized entailment using adaptations of the classical DRed algorithm [128, 506]: when new triples are added, the deducible data is added to the materialization; similarly, when triples are deleted the triples that cannot be deducted anymore are removed from the entailment. The idea of incremental maintenance was previously delivered in the context of deductive databases, where logic programming was used for the incremental maintenance of such entailments. The idea of incrementally maintaining an ontological entailment was proposed
{"title":"Incremental Reasoning on RDF Streams","authors":"Daniele Dell'Aglio, Emanuele Della Valle","doi":"10.1201/b16859-22","DOIUrl":"https://doi.org/10.1201/b16859-22","url":null,"abstract":"The introduction of stream processing methods in the Semantic Web enables the management of data streams on the Web. Chapter 6 introduced models for RDF stream and several extensions of SPARQL engines with windows for stream processing. The chapter assumes the absence of a TBox, so it is possible to compute the query answer without considering the ontology entailment defined through a TBox described in an ontological language. In this chapter, we relax this constraint and we consider the case of query answering over RDF streams when the TBox is not empty. In particular, we focus on Stream Reasoning [544], the topic that studies how to compute and incrementally maintain the ontological entailments in RDF streams. In traditional Semantic Web reasoning data are usually static or quasistatic1, so the whole computation of the ontological entailment can be executed every time the data change. When we consider RDF streams the static hypothesis is not valid anymore: RDF stream engines work with highly dynamic data and they need to process them faster than new data arrives to avoid congestion states. In this scenario, traditional materialization techniques could fail; a possible solution is the incremental maintenance of the materialized entailment using adaptations of the classical DRed algorithm [128, 506]: when new triples are added, the deducible data is added to the materialization; similarly, when triples are deleted the triples that cannot be deducted anymore are removed from the entailment. The idea of incremental maintenance was previously delivered in the context of deductive databases, where logic programming was used for the incremental maintenance of such entailments. The idea of incrementally maintaining an ontological entailment was proposed","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131656687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Aligning Ontologies of Linked Data","authors":"Rahul Parundekar, Craig A. Knoblock, J. Ambite","doi":"10.1201/b16859-4","DOIUrl":"https://doi.org/10.1201/b16859-4","url":null,"abstract":"1.","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129513749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Heitmann, Richard Cyganiak, Conor Hayes, S. Decker
Before the emergence of RDF and Linked Data, Web applications have been designed around relational database standards of data representation and service. A move to an RDF-based data representation introduces challenges for the application developer in rethinking the Web application outside the standards and processes of database-driven Web development. These include, but are not limited to, the graph-based data model of RDF [171], the Linked Data principles [83], and formal and domain specific semantics [96]. To date, investigating the challenges which arise from moving to RDFbased data representation has not been given the same priority as the research on potential benefits. We argue that the lack of emphasis on simplifying the development and deployment of Linked Data and RDF-based applications has been an obstacle for real-world adoption of Semantic Web technologies and the emerging Web of Data. However, it is difficult to evaluate the adoption of Linked Data and Semantic Web technologies without empirical evidence. Towards this goal, we present the results of a survey of more than 100 RDF-based applications, as well as a component-based, conceptual architecture for Linked Data applications which is based on this survey. We also use
{"title":"Architecture of Linked Data Applications","authors":"B. Heitmann, Richard Cyganiak, Conor Hayes, S. Decker","doi":"10.1201/b16859-5","DOIUrl":"https://doi.org/10.1201/b16859-5","url":null,"abstract":"Before the emergence of RDF and Linked Data, Web applications have been designed around relational database standards of data representation and service. A move to an RDF-based data representation introduces challenges for the application developer in rethinking the Web application outside the standards and processes of database-driven Web development. These include, but are not limited to, the graph-based data model of RDF [171], the Linked Data principles [83], and formal and domain specific semantics [96]. To date, investigating the challenges which arise from moving to RDFbased data representation has not been given the same priority as the research on potential benefits. We argue that the lack of emphasis on simplifying the development and deployment of Linked Data and RDF-based applications has been an obstacle for real-world adoption of Semantic Web technologies and the emerging Web of Data. However, it is difficult to evaluate the adoption of Linked Data and Semantic Web technologies without empirical evidence. Towards this goal, we present the results of a survey of more than 100 RDF-based applications, as well as a component-based, conceptual architecture for Linked Data applications which is based on this survey. We also use","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115869784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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