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Query Evaluation on Probabilistic RDF Databases

机译:概率RDF数据库查询评估

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Over the last few years, RDF has been used as a knowledge representation model in a wide variety of domains. Some domains are full of uncertainty. Thus, it is desired to process and manage probabilistic RDF data. The core operation of queries on an RDF probabilistic database is computing the probability of the result to a query. In this paper, we describe a general framework for supporting SPARQL queries on probabilistic RDF databases. In particular, we consider transitive inference capability for RDF instance data. We show that the find operation for an atomic query with the transitive property can be formalized as the problem of computing path expressions on the transitive relation graph and we also propose an approximate algorithm for computing path expressions efficiently. At last, we implement and experimentally evaluate our approach.
机译:在过去几年中,RDF已被用作各种域中的知识表示模型。一些域名充满了不确定性。因此,期望处理和管理概率的RDF数据。 RDF概率数据库上查询的核心操作是将结果的概率计算到查询。在本文中,我们描述了一种支持概率RDF数据库上的SparQL查询的一般框架。特别是,我们考虑RDF实例数据的传递推理能力。我们表明,具有传递属性的原子查询的查找操作可以正义为在传递关系图上计算路径表达的问题,并且我们还提出了一种有效计算路径表达的近似算法。最后,我们实施并通过实验评估我们的方法。

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