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Voice of the Customer Oriented New Product Synthesis Over Knowledge Graphs

[+] Author Affiliations
Feiwei Qin

Hangzhou Dianzi University, Hangzhou, China

Hairui Xu, Weicheng Zhang, Lin Yuan, Ming Li, Yusheng Liu

Zhejiang University, Hangzhou, China

Ying Liu

Cardiff University, Cardiff, UK

Yong Chen

Shanghai Jiao Tong University, Shanghai, China

Paper No. DETC2018-85909, pp. V01AT02A032; 11 pages
doi:10.1115/DETC2018-85909
From:
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1A: 38th Computers and Information in Engineering Conference
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5172-2
  • Copyright © 2018 by ASME

abstract

The online shopping has been much easier and popular, and meanwhile brings new challenges and opportunities to the field of product design and marketing sale. On one hand, product manufacturers find it challenging to produce new popularly accepted products to meet the customers’ needs; on the other hand, end customers usually feel it difficult to buy ideal goods that they really want, even if navigating a huge amount of commodities. There are indeed a ‘communication gap’ between the customers and manufacturers.

As an effort to partially resolve the issue, this paper proposes a novel product synthesis approach from ‘voice of the customer’ over product knowledge graphs. Here the voice of customers mainly refer to the buyers’ product reviews from online shopping platforms or blogs, while the product knowledge graph is constructed containing professional hierarchical product knowledge on its properties based on ontological models. Using the technologies of natural language processing, we first extract the customs’ polarities on each specific aspect of a product, which are then transited to design requirements on the product’s design components. Based on the requirement extractions, and the pre-built product knowledge, semantic web and reasoning techniques are utilized to synthesize a novel product that meets more customer needs. Typical case studies on mobile phones from raw online data demonstrate the proposed approach’s performance.

Copyright © 2018 by ASME

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