Answer Graph: Factorization Matters in Large Graphs

Abstract

Our answer-graph method to evaluate SPARQL conjunctive queries (CQs) finds a factorized answer set first, an answer graph, and then finds the embedding tuples from this. This approach can reduce greatly the cost to evaluate CQs. This affords a second advantage: we can construct a cost-based planner. We present the answer-graph approach, and overview our prototype system, Wireframe. We then offer proof of concept via a micro-benchmark over the YAGO2s dataset with two prevalent shapes of queries, snowflake and diamond. We compare Wireframe s performance over these against PostgreSQL, Virtuoso, MonetDB, and Neo4J to illustrate the performance advantages of our answer-graph approach.