Ontology-based similarity for product information retrieval

• This paper investigates semantic similarity measures for product information retrieval based.
• The proposed similarity measures are based on the comparison of classes in an ontology.
• The ontology is obtained with formal concept analysis and an explicit theoretical framework for product representation.
• The proposed ontology-based similarity measures were evaluated with a case study that compares them against human judgment.
• A CBR system for product service systems demonstrated that the proposed ontology-based similarity measures are effective.

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.