Triple Pattern Fragments: A low-cost knowledge graph interface for the Web
Triple Pattern Fragments: A low-cost knowledge graph interface for the Web
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DOI：
10.1016/j.websem.2016.03.003
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发表时间：
2016-03-01
影响因子：
2.5
通讯作者：
Colpaert, Pieter
中科院分区：
文献类型：
--
作者：
Verborgh, Ruben;Vander Sande, Miel;Colpaert, Pieter
Web上成千上万的RDF知识图中存在数十亿个关联数据三元组，但很少有这些图可以从Web应用程序中实时查询。只有有限数量的知识图在可查询界面中可用，并且现有界面在高可用性下托管可能是昂贵的。为了缓解实时可查询关联数据的短缺，我们为服务器设计了一个低成本的Triple Pattern Fragments接口，以及一个客户端算法，用于根据该接口评估SPARQL查询。本文描述了Linked Data Fragments框架，用于分析Linked Data的Web接口，并使用此框架作为定义Triple Pattern Fragments的基础。我们描述了客户端查询单个知识图和联邦。我们的评估证实，这种技术降低了服务器负载，提高缓存的有效性，从而降低成本，以保持高服务器的可用性。这些好处是以增加带宽和更慢但更稳定的查询执行时间为代价的。这些结果证实了轻量级接口与更具表达力的端点相比可以降低知识发布者的成本，同时使应用程序能够以必要的可靠性查询发布者的数据。(C)2016爱思唯尔B.V.保留所有权利。
Billions of Linked Data triples exist in thousands of RDF knowledge graphs on the Web, but few of those graphs can be queried live from Web applications. Only a limited number of knowledge graphs are available in a queryable interface, and existing interfaces can be expensive to host at high availability. To mitigate this shortage of live queryable Linked Data, we designed a low-cost Triple Pattern Fragments interface for servers, and a client-side algorithm that evaluates SPARQL queries against this interface. This article describes the Linked Data Fragments framework to analyze Web interfaces to Linked Data and uses this framework as a basis to define Triple Pattern Fragments. We describe client-side querying for single knowledge graphs and federations thereof. Our evaluation verifies that this technique reduces server load and increases caching effectiveness, which leads to lower costs to maintain high server availability. These benefits come at the expense of increased bandwidth and slower, but more stable query execution times. These results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers' data with the necessary reliability. (C) 2016 Elsevier B.V. All rights reserved.