Domain knowledge

Ontology learning

Mathematics

Domain (mathematical analysis)

Epistemology

Variety (cybernetics)

World Wide Web

Mathematical analysis

Process ontology

Philosophy

Artificial intelligence

Data science

Ontology

Quality (philosophy)

Computer science

Suggested Upper Merged Ontology

ACM Computing Surveys

The number of applications being developed that require access to knowledge about the real world has increased rapidly over the past two decades. Domain ontologies, which formalize the terms being used in a discipline, have become essential for research in areas such as Machine Learning, the Internet of Things, Robotics, and Natural Language Processing, because they enable separate systems to exchange information. The quality of these domain ontologies, however, must be ensured for meaningful communication. Assessing the quality of domain ontologies for their suitability to potential applications remains difficult, even though a variety of frameworks and metrics have been developed for doing so. This article reviews domain ontology assessment efforts to highlight the work that has been carried out and to clarify the important issues that remain. These assessment efforts are classified into five distinct evaluation approaches and the state of the art of each described. Challenges associated with domain ontology assessment are outlined and recommendations are made for future research and applications.

Evaluating Domain Ontologies