Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2017/34:2 PP.173-180
Representation of user requirements is essential for product innovation. However, traditional market survey methods seem to fail to represent user requirements effectively in current big data context. We propose a novel ontology-based method to overcome limitations of existing research on the extraction and representation of users' requirements.This method integrates initial data generation, requirement ontology generation and demand characterization into a complete set of scientific guidance for user demand characterization using natural semantic processing, intelligent machine learning and other artificial intelligence algorithms. It uses ontology and big data processing techniques to extract concepts, taxonomic and non-taxonomic relations from huge raw internet data, and further helps identify user's requirements, especially fuzzy requirements. The identified user requirements can be embodied as product features, which helps build product feature representation library to provide support for new product development and innovation.
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