Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Ontology module extraction for ontology reuse: an ontology engineering perspective
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Inspecting regularities in ontology design using clustering
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Extraction and analysis of the structure of labels in biomedical ontologies
Proceedings of the 2nd international workshop on Managing interoperability and compleXity in health systems
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Detecting planned and unplanned deviations from guidelines that give rise to patterns in ontologies can be difficult, even simply in terms of detecting regularities in a complex artefact. In this paper we demonstrate the usage of RIO; a framework for detecting such syntactic regularities using cluster analysis of the entities in ontologies. We demonstrate its usage with SNOMED-CT, a large medical terminology. We focus on the inspection of three modules from SNOMED-CT and we analyse them in terms of their types and number of regularities and irregularities. The results show that modules of the ontology that did not follow a general pattern contained defects such as missing existential restrictions. In the worst case, the expected patterns described in the technical guide of the ontology were followed by 10% of the corresponding entities in the module. We argue that RIO can provide a means by which the possible deviations of design styles can be found and reported to the domain experts.