ProbMap: Automatically constructing design galleries through feature extraction and semantic clustering

Making sense of large unstructured problem spaces is cognitively demanding. Structure can help, but adding structure to a problem space also takes significant effort. ProbMap is a novel application for automatically constructing a design gallery from unstructured text input. Given a list of problem statements, ProbMap extracts and semantically groups the stakeholders to construct hierarchical search facets which enables designers to more efficiently navigate the problem statements. We contribute a novel feature extraction algorithm using natural language processing and a technique for automatically constructing a design gallery. These stakeholders are grouped semantically by clustering stakeholders with higher pairwise similarity together. Preliminary trials show that these techniques, which mirror traditional design activities like stakeholder identification and affinity mapping, provide an initial structure to a large unstructured problem space. This resulted in similar features that would be extracted by humans and sensible clusters.