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GraSS: An Efficient Method for RDF Subgraph Matching

机译:GraSS:RDF子图匹配的一种有效方法

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Resource Description Framework (RDF) is a standard data model of the Semantic Web, and it has been widely adopted in various domains in recent years for data and knowledge representation. Unlike queries on relational databases, most of queries applied on RDF data are known as graph queries, expressed in the SPARQL language. Subgraph matching, a basic SPARQL operation, is known to be NP-complete. Coupled with the rapidly increasing volumes of RDF data, it makes efficient graph query processing a very challenging problem. This paper primarily focuses on providing an index scheme and corresponding algorithms that support the efficient solution of such queries. We present a subgraph matching query engine based on the FFD-index which is an indexing mechanism encoding a star subgraph into a bit string. A SPARQL query graph is decomposed into several star query subgraphs which can be efficiently processed benefiting from succinct FFD-index data structure. Extensive evaluation shows that our approach outperforms RDF-3X and gStore on solving subgraph matching.
机译:资源描述框架(RDF)是语义Web的标准数据模型,近年来,它已在各个领域中广泛用于数据和知识表示。与关系数据库查询不同,应用于RDF数据的大多数查询都称为图查询,以SPARQL语言表示。子图匹配(一种基本的SPARQL操作)已知是NP完全的。再加上RDF数据量的快速增长,它使有效的图形查询处理成为一个非常具有挑战性的问题。本文主要侧重于提供索引方案和相应的算法,以支持此类查询的有效解决方案。我们提出了一种基于FFD-index的子图匹配查询引擎,该引擎是将星形子图编码为位字符串的索引机制。将SPARQL查询图分解为几个星形查询子图,这些子图可以借助简洁的FFD索引数据结构得到有效处理。广泛的评估表明,在解决子图匹配方面,我们的方法优于RDF-3X和gStore。

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