Statistical Knowledge Patterns for Characterising Linked Data

Knowledge Patterns (KPs), and even more specifically Ontology Design Patterns (ODPs), are no longer only generated in a top-down fashion, rather patterns are being extracted in a bottom-up fashion from online ontologies and data sources, such as Linked Data. These KPs can assist in tasks such as making sense of datasets and formulating queries over data, including performing query expansion to manage the diversity of properties used in datasets. This paper presents an extraction method for generating what we call Statistical Knowledge Patterns (SKPs) from Linked Data. SKPs describe and characterise classes from any reference ontology, by presenting their most frequent properties and property characteristics, all based on analysis of the underlying data. SKPs are stored as small OWL ontologies but can be continuously updated in a completely automated fashion. In the paper we exemplify this method by applying it to the classes of the DBpedia ontology, and in particular we evaluate our method for extracting range axioms from data. Results show that by setting appropriate thresholds, SKPs can be generated that cover (i.e. allow us to query, using the properties of the SKP) over 94% of the triples about individuals of that class, while only needing to care about 27% of the total number of distinct properties that are used in the data.