Labeling RDF Graphs for Linear Time and Space Querying

Abstract

Abstract. Indices and data structures for web querying have mostly considered tree shaped data, reflecting the view of XML documents as tree-shaped. However, for RDF (and when querying ID/IDREF constraints in XML) data is indisputably graph-shaped. In this chapter, we first study existing indexing and labeling schemes for RDF and graph data in general with focus on support for efficient adjacency and reachability queries. For XML, labeling schemes are an important part of the widespread adoption of XML, in particular for mapping XML to existing (relational) database technology. However, the existing indexing and labeling schemes for RDF (and graph data in general) sacrifice one of the most attractive properties of XML labeling schemes, the constant time (and per-node space) test for adjacency (child) and reachability (descendant). In the second part, we introduce the first labeling scheme for RDF data that retains this property and thus achieves linear time and space processing of acyclic RDF queries on a significantly larger class of graphs than previous approaches (which are mostly limited to tree-shaped data). Finally, we show how this labeling scheme can be applied to (acyclic) SPARQL queries to obtain an evaluation algorithm with time and space complexity linear in the number of resources in the queried RDF graph.

Storing and Indexing Massive RDF Datasets

“…As such, these hierarchies are stored separately by Matono et al [51,52]. Further study of interval-based labeling schemes for RDF graphs with and without cycles has been undertaken by Furche et al [21].…”

Efficient path query and reasoning method based on rare axis

“…Moreover, NRE is also proved to have strong expression ability and polynomial time evaluation complexity [12] . However, with the explosive growth of RDF graph data, even the polynomial time evaluation complexity cannot meet the path query requirements on the web-scale RDF graph data [13, 14] .In order to further improve the query efficiency of NRE on the large-scale RDF graph data, we first use distributed technology to build the navigation axis index of NRE on RDF graph data, and then make a statistics on the frequency of NRE navigation axis in the index. As the frequency of different navigation axes appearing in the NRE index on RDF graph is different, and when those low frequency navigation axes appear in the NRE expression, the NRE expression can be cut from these axes to reduce the search scope of RDF graph, thereby reducing the time and space evaluation complexity of NRE.…”

“…Moreover, NRE is also proved to have strong expression ability and polynomial time evaluation complexity [12] . However, with the explosive growth of RDF graph data, even the polynomial time evaluation complexity cannot meet the path query requirements on the web-scale RDF graph data [13, 14] .…”