Schema-agnostic SPARQL-driven faceted search benchmark generation

In this work, we present a schema-agnostic faceted browsing benchmark generation framework for RDF data and SPARQL engines. Faceted search is a technique that allows narrowing down sets of information items by applying constraints over their properties, whereas facets correspond to properties of these items. While our work can be used to realise real-world faceted search user interfaces, our focus lies on the construction and benchmarking of faceted search queries over knowledge graphs. The RDF model exhibits several traits that seemingly make it a natural foundation for faceted search: all information items are represented as RDF resources, property values typically already correspond to meaningful semantic classifications, and with SPARQL there is a standard language for uniformly querying instance and schema information.

However, although faceted search is ubiquitous today, it is typically not performed on the RDF model directly. Two major sources of concern are the complexity of query generation and the query performance. To overcome the former, our framework comes with an intermediate domain-specific language. Thereby our approach is SPARQL-driven which means that every faceted search information need is intensionally expressed as a single SPARQL query. In regard to the latter, we investigate the possibilities and limits of real-time SPARQL-driven faceted search on contemporary triple stores. We report on our findings by evaluating systems performance and correctness characteristics when executing a benchmark generated using our generation framework.

All components, namely the benchmark generator, the benchmark runners and the underlying faceted search framework, are published freely available as open source.