Similarity-based ontology alignment in OWL-Lite

TLDR

Interoperability of heterogeneous Web systems depends on aligning underlying ontologies, a task that grows more complex with richer description languages and therefore requires sophisticated tools, such as similarity‑based approaches that are powerful and flexible for OWL ontologies. The study defines a universal similarity measure for comparing entities across two ontologies, based on entity type and all defining features such as superclasses, properties, and instances. This measure handles one‑to‑many relationships and circular entity descriptions by locally matching entity sets and iteratively computing recursively dependent similarities.

Abstract

Interoperability of heterogeneous systems on the Web will be admittedly achieved through an agreement between the underlying ontologies. However, the richer the ontology description language, the more complex the agreement process, and hence the more sophisticated the required tools. Among current ontology alignment paradigms, similarity-based approaches are both powerful and flexible enough for aligning ontologies expressed in languages like OWL. We define a universal measure for comparing the entities of two ontologies that is based on a simple and homogeneous comparison principle: Similarity depends on the type of entity and involves all the features that make its definition (such as superclasses, properties, instances, etc.). One-to-many relationships and circularity in entity descriptions constitute the key difficulties in this context: These are dealt with through local matching of entity sets and iterative computation of recursively dependent similarities, respectively.