Creative Exploration Using Topic Based Bisociative Networks

TL;DR

This paper introduces a computational framework that uses topic models and bisociative networks to discover novel cross-domain connections, fostering creativity and inspiration in design by identifying conceptual bridges among ideas.

Contribution

It proposes a new method combining topic modeling and bisociative networks to enhance creative discovery across multiple domains using large text datasets.

Findings

Discovered that conceptual bridges are perceived as more novel by users.

Showed that the network helps find cross-domain inspiration effectively.

Validated usefulness with a large dataset from OpenIDEO.

Abstract

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.