Semantic networks for enhancement of creativity

Creativity generates novel ideas to solve real-world problems and grants us the power to transform the surrounding world beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. The origin of human creativity, however, is poorly understood, and quantitative semantic measures that could predict the future success of newly generated ideas are currently unknown.

In our recent article published in Knowledge-Based Systems, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sanchez–Batet) and semantic similarity (Lin/Sanchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence.