RDF pattern matching using sortable views

Abstract

In the last few years, RDF is becoming the dominating data model used in semantic web for knowledge representation and inference. In this paper, we revisit the problem of pattern matching query in RDF model, which is usually expensive in efficiency due to the huge cost on join operations. To alleviate the efficiency pain, view materialization techniques are usually deployed to accelerate the query processing. However, given an arbitrary view, it remains difficult to identify how to reuse the view for a particular query, because of the NP-hardness behind the algorithm matching patterns and views. To fully exploit the benefit of the materialized views, we propose a new paradigm to enhance the effectiveness of the materialized view. Instead of choosing materialized views in arbitrary form, our paradigm aims to select the views only if they are sortable. The property of sortability raises huge gains on the pattern-view matching, bringing down the cost to linear complexity in terms of the pattern size. On the other side, the costs on identifying sortable views and searching over the views using inverted index are affordable. Moreover, sortable views generally improve the overall performance of pattern matching, by means of a cost model used to optimize the query rewriting on the most appropriate views. Finally, we demonstrate extensive experimental results to verify the superiority of our proposal on both efficiency and effectiveness.

Adaptive Partitioning for Very Large RDF Data

“…Materialized views: Several works attempt to speed up the execution of SPARQL queries by materializing a set of views [6,14] or a set of path expressions [10]. The selection of views is based on a representative workload.…”

Subgraph Search in Large Graphs with Result Diversification

“…For example, TALE [28] by Tian et al first locates mappings of a subset of "important" nodes and then expands them to the whole graph. For specific applications, there are matching algorithms for RDF query [6], image patch search [7], graph nodes with multiple labels [34], labels with ontology structure [33], querying probabilistic database [38]. These algorithms rank the subgraph search results by similarity score.…”

“…finding a subgraph mapping in a target graph which is similar to the query graph, is an important primitive for searching and extracting information in the graph database. The problem has applications in cheminformatics, bioinformatics [27] where one wants to find a specific component from a compound, image patch search [7], RDF query in semantic web [6], and social network such as the facebook graph search 1 . We ask the question on how to efficiently find the top k similar subgraph mappings and effectively rank the results to improve the search quality.…”