Creative Exploration Using Topic Based Bisociative Networks
This work addresses the challenge of fostering creativity in design by providing computational tools for bisociative discovery, though it is incremental in applying existing topic modeling techniques to a new context.
The paper tackled the problem of generating creative insights by connecting disparate domains, proposing a topic-based bisociative network framework that uses topic models on unstructured text ideas to discover cross-domain links, and showed that these links are perceived as more novel and useful for new idea generation.
Bisociative knowledge discovery is an approach that combines elements from two or more "incompatible" domains to generate creative solutions and insight. Inspired by Koestler's notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network--- a graph that captures conceptual similarity between ideas--- that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation.