Object Similarity in Ontologies: A Foundation for Business Intelligence Systems and High-Performance Retrieval

Finding good algorithms for assessing the similarity of complex objects in ontologies is central to the functioning of techniques such as retrieval, matchmaking, clustering, data-mining, semantic sense disambiguation, ontology translations, and simple object comparisons. These techniques provide the basis for supporting a wide variety of business intelligence computing tasks like finding a process in a best practice repository, finding a suitable service provider or outsourcing partner for agile process enactment, dynamic customer segmentation, semantic web applications, and systems integration. To our knowledge, however, there exists no study that systematically compares the prediction quality of ontology-based similarity measures. This paper assembles a catalogue of ontology-based similarity measures that are (partially) adapted from related domains. These measures are compared to each other within a large, real-world best practice ontology as well as evaluated in a realistic business process retrieval scenario. We find that different similarity algorithms reflect different notions of similarity. We also show how a combination of similarity measures can be used to improve both precision and recall of an ontology-based, query-by-example style, object-retrieval approach. Combining the study’s findings with the literature, we argue for the need of extended studies to assemble a more complete catalogue of object similarity measures that can be evaluated in many applications and ontologies.