One paper is about inferring the shape of the output of CONSTRUCT SPARQL queries by axiomatizing queries with Description Logics, and the other is about computing how-provenance for SPARQL queries.
From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries
SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes, allowing users to understand which data they can expect and which they cannot. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data. In this paper, the authors study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. They propose and implement an algorithm based on Description Logics, which analyses input SHACL shapes and a CONSTRUCT query and returns the SHACL constraints that the output data satisfies.
P. Seiffer, D. Hernández, R. Lämmel, S. Staab. From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. In The ACM Web Conference 2024, WWW 2024, Singapore, 13–17 May 2024.
NPCS: Native Provenance Computation for SPARQL
Several knowledge graph-based applications, such as trust assessment or view maintenance on dynamic data, rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. The authors of this article propose NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. Thanks to its reliance on query rewriting techniques, the approach directly applies to already deployed SPARQL engines using different reification schemes – including RDF*. The authors show that their approach brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.
Z. Asma, D. Hernández, L. Galárraga, G. Flouris, I. Fundulaki, K. Hose. NPCS: Native Provenance Computation for SPARQL. In The ACM Web Conference 2024, WWW 2024, Singapore, 13–17 May 2024.