An Ecosystem for Ontology Interoperability

TL;DR

This paper introduces an ecosystem that integrates advanced semantic techniques across the ontology engineering lifecycle to improve interoperability and data integration in knowledge graphs, demonstrated through a building domain case study.

Contribution

It presents a novel ecosystem combining ontology design patterns, matching, versioning, and data-driven validation to enhance ontology interoperability.

Findings

Improved interoperability in knowledge graphs.

Effective integration of semantic techniques across OE lifecycle.

Validated approach through a sensor observation case study.

Abstract

Ontology interoperability is one of the complicated issues that restricts the use of ontologies in knowledge graphs (KGs). Different ontologies with conflicting and overlapping concepts make it difficult to design, develop, and deploy an interoperable ontology for downstream tasks. We propose an ecosystem for ontology interoperability. The ecosystem employs three state-of-the-art semantic techniques in different phases of the ontology engineering (OE) life cycle: ontology design patterns (ODPs) in the design phase, ontology matching and versioning (OM\&OV) in the develop phase, and data-driven ontology validation (DOVA) in the deploy phase, to achieve better ontology interoperability and data integration in real-world applications. A case study of sensor observation in the building domain validates the usefulness of the proposed ecosystem.