Eliciting user needs from mass online reviews is playing a significant role in the product iteration process. Efficient user needs elicitation does achieve considerable benefits for maintaining higher competitiveness and a speedier lifecycle. However, there is inevitably an online review scarcity about new products due to the short time on the market and low buyer recognition compared with commonly used products. This paper proposes a small sample data-driven method for user needs elicitation from online reviews in new product iteration. In the first stage, a scraped initial online review dataset is pre-processed roughly to improve the data quality. And then, reviews are classified into multiple categories according to different topics using ERNIE. In the second stage, each topic-based dataset is reprocessed in detail. Thereafter, the key user needs set is determined and facilitated by extracting key product information phrases from every single dataset using improved SIFRank. Moreover, the case study of a smart cat feeder is carried out to demonstrate the feasibility and potential of the ERNIE-ISIFRank methodology. Finally, comparative experiments are conducted to verify the advantages of the proposed method which is primarily based on the pre-trained language model to enhance the deep understanding of the semantics of online reviews. The experimental results confirm that the proposed method can assist in identifying key user needs with high efficiency.

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Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain.

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Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage.

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Product service systems (PSS) - integration of products and services - with aims to achieve economic profit and reduce environmental impacts, is a hot issue in academia. The purpose of this study is to comprehend the state-of-the-art in the field of PSS design, evaluation, and operation methodologies (PSS-DEOM) by conducting a systematic literature review. Up to 258 publications related to PSS-DEOM were reviewed and divided into three categories: PSS design methodologies (PSS-DM), PSS evaluation methodologies (PSS-EM), and PSS operation methodologies (PSS-OM). Based on the findings, future research trends were proposed and discussed.@en