University of Oulu

Georgiev, G., Georgiev, D. (2018) Enhancing user creativity: Semantic measures for idea generation. Knowledge-Based Systems, 151, 1-15. doi:10.1016/j.knosys.2018.03.016

Enhancing user creativity : semantic measures for idea generation

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Author: Georgiev, Georgi V.1; Georgiev, Danko D.2
Organizations: 1Center for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu
2Institute for Advanced Study, Varna, Bulgaria
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2018060525264
Language: English
Published: Elsevier, 2018
Publish Date: 2018-06-05
Description:

Abstract

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes 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. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated ideas are currently unknown. Here, 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 Sánchez–Batet) and semantic similarity (Lin/Sánchez–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.

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Series: Knowledge-based systems
ISSN: 0950-7051
ISSN-E: 1872-7409
ISSN-L: 0950-7051
Volume: 151
Pages: 1 - 15
DOI: 10.1016/j.knosys.2018.03.016
OADOI: https://oadoi.org/10.1016/j.knosys.2018.03.016
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
222 Other engineering and technologies
515 Psychology
616 Other humanities
3112 Neurosciences
Subjects:
Funding: G.V.G. acknowledges partial financial support for this study from Grant-in-Aid 25750001 by the Japan Society for the Promotion of Science (JSPS).
Copyright information: © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.
  https://creativecommons.org/licenses/by/4.0/