Property graphs may be constrained by schemas that inform both query engines and human users about the shape of valid data, enforcing a contract between data provider and consumer. Composable property-graph queries transform input graphs into output graphs, raising the question of what schema can be expected after one or several transformation steps.
We investigate how schema constraints can be inferred given an input schema and a transforming query. Specifically, we propose a reasoning procedure that, given an input schema in ProGS and a query in G-CORE, infers an output schema. Since graph updates occur frequently, our inference procedure does not rely on graph instances, thus the computed output schema applies to all graphs originating from any input graph complying with the input schema.
Related work has addressed this problem for SPARQL CONSTRUCT queries, encoding it in Description Logics (DLs) so that the output schema is entailed by axioms inferred from input schemas and queries. Property graphs and their queries complicate the matter, as property graphs feature label and property annotations as well as first-class edges. Thus, reification has to be used in one way or another, though available DLs lack the means to encode such features directly. We approach this novel challenge via a family of mappings for i) property graphs reified in RDF, aligned with ii) a mapping from ProGS to SHACL and iii) a mapping from G-CORE to SPARQL CONSTRUCT queries. In this manner, schema inference for property graphs becomes manageable, as we break apart the problem through the extra mapping layer and utilize efficient DL reasoners.
We develop the metatheory regarding the soundness of inferred schema constraints and the semantic equivalence of mapped schemas and queries.
Blogger's Review: This paper delves into the complexities of schema inference for property graphs, proposing a reasoning framework that addresses the challenges of graph queries through effective mapping layers, which holds significant theoretical and practical implications for researchers and developers interested in graph databases and query optimization.