Sources of inspiration help designers to define the context of their designs and reflect on the emotional impact of their new products. By observing and interpreting sources of inspiration, designers form vocabularies of terms, pallets of colors, or mood boards with images, which express their feelings, inspire their creativity and help them communicate design concepts. These ideas are the motivation behind the EU-funded project TRENDS, which aimed at developing a software tool that supports the inspirational stage of design by providing designers of concept cars with various sources of inspiration. This paper concentrates on OntoTag, the semantic-based image retrieval algorithm developed within the TRENDS project, and its evaluation. OntoTag uses concepts from a general-purpose lexical ontology called OntoRo, and semantic adjectives from a domain-specific ontology for designers called CTA, to index the images in the TRENDS database in a way which provides designers with a degree of serendipity and stimulates their creativity. The semantic-based algorithm involves the following four steps: (i) creating a collection of documents and images retrieved from the web, (ii) for each document, identifying the most frequently used keywords and phrases in the text around the image, (iii) identifying the most powerful concepts represented in each document, and (iv) ranking the concepts identified and linking them to the images in the collection. OntoTag differs significantly from earlier approaches as it does not rely on machine learning and the availability of tagged corpuses. Its main innovation is in the use of the words’ monosemy and polysemy as a measure of their probability to belong to a certain concept. The proposed approach is illustrated with examples based on the software tool developed for the needs of two of the industrial collaborators involved in the TRENDS project.
Skip Nav Destination
e-mail: carole.bouchard@paris.ensam.fr
Article navigation
September 2010
Research Papers
In Search of Design Inspiration: A Semantic-Based Approach
Carole Bouchard
Carole Bouchard
Laboratory of New Product Design and Innovation,
e-mail: carole.bouchard@paris.ensam.fr
Ecole Nationale Supérieure des Arts et Métiers
, 75013 Paris, France
Search for other works by this author on:
Rossitza Setchi
Carole Bouchard
Laboratory of New Product Design and Innovation,
Ecole Nationale Supérieure des Arts et Métiers
, 75013 Paris, Francee-mail: carole.bouchard@paris.ensam.fr
J. Comput. Inf. Sci. Eng. Sep 2010, 10(3): 031006 (23 pages)
Published Online: September 3, 2010
Article history
Received:
July 31, 2009
Revised:
July 7, 2010
Online:
September 3, 2010
Published:
September 3, 2010
Citation
Setchi, R., and Bouchard, C. (September 3, 2010). "In Search of Design Inspiration: A Semantic-Based Approach." ASME. J. Comput. Inf. Sci. Eng. September 2010; 10(3): 031006. https://doi.org/10.1115/1.3482061
Download citation file:
Get Email Alerts
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
J. Comput. Inf. Sci. Eng
Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
J. Comput. Inf. Sci. Eng (March 2025)
MODAL-DRN-BL: A Framework for Modal Analysis Based on Dilated Residual Broad Learning Networks
J. Comput. Inf. Sci. Eng (March 2025)
Related Articles
A Methodology for Creating Ontologies for Engineering Design
J. Comput. Inf. Sci. Eng (June,2007)
Digital Twins: Review and Challenges
J. Comput. Inf. Sci. Eng (June,2021)
Developing Engineering Ontology for Information Retrieval
J. Comput. Inf. Sci. Eng (March,2008)
Ontologies for Supporting Engineering Design Optimization
J. Comput. Inf. Sci. Eng (June,2007)
Related Proceedings Papers
Related Chapters
Ontology Engineering: Semantic Web Technology in Aspect of Software Engineering
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
SESR: Semantic Entity Extraction for Computing Semantic Relatedness
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
Visual Representation of Hierarchy of Attributes and Concepts as Ontology for Semantic Reasoning
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20