Design Rationale Knowledge Management: A Survey

Yue, Gaofeng; Liu, Jihong; Hou, Yanze

doi:10.1007/978-3-030-00560-3_33

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…DR is important design knowledge about the design process, which includes issues, intents, alternatives, pros and cons of the design. DR plays a very important role in engineering design, such as facilitating collaborative design [6], [7], [8], [9], [10], [11], helping designers with personal knowledge management, assisting in relevant education and training [12], implementing root cause analysis to track design failures [13], [14]. If DR could be automatically extracted from patent documents and formally stored, it would be of great value to designers, because the extracted DR can inspire innovative designs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Patent Knowledge Extraction Method for Innovative Design

et al. 2023

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As an important source of inspiration, the great number of patent documents provides designers with valuable knowledge of design rationale (DR), including issues, intent, pros and cons of the solutions. Researchers have carried out a number of data analysis studies based on patent information, which is now a new discipline called Patinformatics, including the analysis of patent information from a macro perspective and the identification and extraction of patent knowledge from a micro perspective. If DR knowledge could be extracted automatically from the patent documents and provided to designers as a source of inspiration, it would greatly promote innovative design, and at the same time promote the reuse of patent documents and the wide application of DR theory, which can be like killing three birds with one stone. To address this issue, this study proposes an improved lexical-syntactic pattern method for DR centric patent knowledge extraction, including DR Vector Space model (DRVS), DRV Trigger Word (DRV-TW), Design Rationale Vector (DRV), DR credibility (DRC) and others, and DRV based knowledge extraction algorithms. Knowledge extraction experiments were conducted on 1491 patent documents to verify the feasibility and performance of the method. In addition, two other sets of comparative experiments were conducted using the FastText and BERT machine learning methods, and the results further confirmed the reliability of the proposed method for lowresource corpus.INDEX TERMS Patent analysis, design rationale, knowledge extraction, design knowledge network.

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Section: Introductionmentioning

confidence: 99%

A Novel Patent Knowledge Extraction Method for Innovative Design

et al. 2023

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“…Several approaches on design rationale have been proposed, and a few surveys have been conducted. We summarize here relevant findings from the surveys by Yue et al (2018), Jarczyk et al (1992), and Regli et al (2000). Kunz and Rittel (1970) proposed IBIS (Issue Based Information Systems) as a means to support coordination and planning of political decision process.…”

Section: Design Rationalementioning

confidence: 99%