Triag, a framework based on triangles of RDF triples
Hubert Naacke, Olivier Curé · 2020
The success of RDF-based enterprise Knowledge Graphs partly depends on the efficiency to serve SPARQL queries over large datasets. This usually requires the optimization of a large number of joins between a query's triple patterns. A common solution to this problem is to index triples in several orders and to provide adapted query processing optimizations. In this paper, we extend this approach by proposing a framework that tackles a frequently encountered basic graph pattern: triangles. We present appropriate data structures to store these triangles, provide distributed algorithms to discover and materialize them (including inferred triangles), and detail query optimization techniques. Experimental results conducted over an Apache Spark implementation on two real-world RDF datasets emphasize the performance boost obtained with our approach.