Ontology-based similarity for product information retrieval

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 nonnumeric 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

Integration of linked open data in case-based reasoning systems

This paper discusses the opportunities of integrating Linked Open Data (LOD) resources into Case-Based Reasoning (CBR) systems. Upon the application domain travel medicine, we will exemplify how LOD can be used to fill three out of four knowledge containers a CBR system is based on. The paper also presents the applied techniques for the realization and demonstrates the performance gain of knowledge acquisition by the use of LOD

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN