Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets
This addresses the challenge for ontology engineers by providing an interruptible tool to automate ontology extension, though it is incremental as it builds on existing mining techniques.
The paper tackles the problem of extending ontologies by mining OWL 2 EL class expressions directly from online RDF datasets, resulting in an algorithm that downloads small parts of data and mines axioms, with a crowdsourcing experiment showing most axioms are correct.
In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner are correct and can be added to an ontology. We provide a ready to use Protégé plugin implementing the algorithm, to support ontology engineers in their daily modeling work.