Efficient Computation of Signature-Restricted Views for Semantic Web Ontologies

Uniform Interpolation (UI) is an advanced reasoning service used to narrow down an ontology to a restricted view. This new ontology, known as a uniform interpolant, will only consist of the ''relevant names'', yet it will retain their original meanings. UI is immensely promising due to its applicability across various domains where custom views of ontologies are essential. Nonetheless, to unlock its full potential, we need optimized techniques to generate these tailored views. Previous studies suggest that creating uniform interpolants for EL-ontologies is notably challenging. In some instances, it is not even feasible to compute a uniform interpolant; when feasible, the size of the uniform interpolant can be up to triple exponentially larger than the source ontology. Despite these challenges, our paper introduces an improved ''forgetting'' technique specifically designed for computing uniform interpolants of ELI-ontologies. We demonstrate that, with good normalization and inference strategies, such uniform interpolants can be efficiently computed, just as quickly as computing ''modules''. A comprehensive evaluation with a prototypical implementation of the method shows superb success rates over two popular benchmark datasets, demonstrating a clear computational advantage over state-of-the-art approaches.