Tracking RDF Graph Provenance using RDF Molecules ? ( Revision 2 )
Tracking RDF Graph Provenance using RDF Molecules ? ( Revision 2 )
复制标题
使用 RDF 分子跟踪 RDF 图来源？
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
McGuinness
中科院分区：
文献类型：
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作者：
Li Ding;Timothy W. Finin;Yun Peng;Paulo Pinheiro da Silva;L. Deborah;McGuinness
语义网有助于整合部分知识，并从网络知识源中找到假设的证据。然而，用于跟踪RDF图的起源的适当粒度级别仍然存在争议。RDF文档太粗糙，因为它可能包含不相关的信息。当两个三元组共享同一个空节点时，RDF三元组将失败。因此，本文研究了RDF图的无损分解，并使用RDF分子跟踪RDF图的起源，RDF分子是RDF图的最细和无损组件。一个子图是无损的，如果它可以用来恢复原始图，而不引入新的三元组。一个子图是最好的，如果它不能进一步分解成无损子图。无损分解算法和RDF分子已经被形式化，并在SwoCulture项目中的RDF图起源服务原型中实现。
The Semantic Web facilitates integrating partial knowledge and finding evidence for hypothesis from web knowledge sources. However, the appropriate level of granularity for tracking provenance of RDF graph remains in debate. RDF document is too coarse since it could contain irrelevant information. RDF triple will fail when two triples share the same blank node. Therefore, this paper investigates lossless decomposition of RDF graph and tracking the provenance of RDF graph using RDF molecule, which is the finest and lossless component of an RDF graph. A sub-graph is lossless if it can be used to restore the original graph without introducing new triples. A sub-graph is finest if it cannot be further decomposed into lossless sub-graphs. The lossless decomposition algorithms and RDF molecule have been formalized and implemented by a prototype RDF graph provenance service in Swoogle project.