Abstract
The lack of a descriptive schema for an RDF dataset has motivated several research works addressing the problem of automatic schema discovery. The goal of these approaches is to generate a structural schema of a given RDF dataset from its instances. However, as new instances are added, the generated schema may become inconsistent with the dataset.
In this paper, we propose an incremental schema discovery approach for massive RDF datasets. It is based on a scalable and incremental density-based clustering algorithm which propagates the changes occurring in the dataset into the clusters corresponding to the classes of the schema. Our approach is implemented using big data technology to scale up schema discovery while providing a high quality clustering result. We present some experiments which demonstrate the efficiency of our proposal on both synthetic and real datasets.
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Bouhamoum, R., Kedad, Z., Lopes, S. (2021). Incremental Schema Discovery at Scale for RDF Data. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_12
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