Ephedra: Efficiently Combining RDF Data and Services Using SPARQL Federation

Knowledge graph management use cases often require addressing hybrid information needs that involve multitude of data sources, multitude of data modalities (e.g., structured, keyword, geospatial search), and availability of computation services (e.g., machine learning and graph analytics algorithms). Although SPARQL queries provide a convenient way of expressing data requests over RDF knowledge graphs, the level of support for hybrid information needs is limited: existing query engines usually focus on retrieving RDF data and only support a set of hard-coded built-in services. In this paper we describe representative use cases of metaphacts in the cultural heritage and pharmacy domains and the hybrid information needs arising in them. To address these needs, we present Ephedra: a SPARQL federation engine aimed at processing hybrid queries. Ephedra provides a flexible declarative mechanism for including hybrid services into a SPARQL federation and implements a number of static and runtime query optimization techniques for improving the hybrid SPARQL queries performance. We validate Ephedra in the use case scenarios and discuss practical implications of hybrid query processing.