Information representation in engineering design is currently dominated by top–down approaches such as taxonomies and ontologies. While top–down approaches provide support for computational reasoning, they are primarily limited due to their static nature, limited scope, and developer-centric focus. Bottom–up approaches, such as folksonomies, are emerging as means to address the limitations of top–down approaches. Folksonomies refer to collaborative classification by users who freely assign tags to design information. They are dynamic in nature, broad in scope, and are user focused. However, they are limited due to the presence of ambiguities and redundancies in the tags used by different people. Considering their complementary nature, the ideal approach is to use both top–down and bottom–up approaches in a synergistic manner. To facilitate this synergy, the goal in this paper is to present techniques for using dynamic folksonomies to extract global characteristics of the structure of design information, and to create hierarchies of tags that can guide the development of structured taxonomies and ontologies. The approach presented in this paper involves using (a) tools such as degree distribution and K-neighborhood connectivity analysis to extract the global characteristics of folksonomies and (b) set-based technique and hierarchical clustering to develop a hierarchy of tags. The approach is illustrated using data from a collective innovation platform that supports collaborative tagging for design information. It is shown that despite the flat nature of the folksonomies insights about the hierarchy in information can be gained. The effects of various parameters on the tag hierarchy are discussed. The approach has potential to be used synergistically with top–down approaches such as ontologies to support the next generation collaborative design platforms.
Computational Reasoning across Multiple Models
