Scalable Exploratory Search on Knowledge Graphs Using Apache Spark

Faceted search is a popular exploratory search paradigm on Big Knowledge Graphs. Translating exploration steps into database queries for processing leads to several joins when dealing with knowledge graphs as opposed to filter conditions when dealing with structured data. Further, existing engines handle each exploration step as independent queries in spite of data dependencies that often exist between steps. In this work, we propose an incremental query execution model RAPIDFacet, that exploits the iterative nature of faceted search and reuses intermediate results. The approach is built on top of Apache Spark which naturally supports iterative models and the Nested Triplegroup Data Model and Algebra (NTGA) which uses a coarse grained data model to avoid joins. Evaluations showed up to 150x faster execution than existing approaches.