Mining Validating Shapes for Large Knowledge Graphs via Dynamic Reservoir Sampling

Abstract

Knowledge Graphs (KGs) are databases that model knowledge from heterogeneous domains using the graph data model. Shape constraint languages have been adopted in KGs to ensure their data quality. They encode the equivalent of a schema in the Resource Description Framework (RDF). Unfortunately, few KGs are accompanied by a corresponding set of validating shapes. When validating shapes are missing, the solution is to extract them from the graph via mining techniques. Current shape extraction methods are often incomplete, not scalable, and generate spurious shapes. Thus, in this discussion paper, we present our recent contribution: a novel Quality Shapes Extraction (QSE) method for large graphs. QSE computes confidence and support for shape constraints via a novel Dynamic Reservoir Sampling method, enabling the identification of informative and reliable shapes. QSE is the first method (validated on WikiData and DBpedia) to extract a complete set of shapes from large real-world KGs.