A Novel Patent Knowledge Extraction Method for Innovative Design
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9987471/ |
| _version_ | 1797956359464943616 |
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| author | Gaofeng Yue Jihong Liu Yongzhu Hou Qiang Zhang |
| author_facet | Gaofeng Yue Jihong Liu Yongzhu Hou Qiang Zhang |
| author_sort | Gaofeng Yue |
| collection | DOAJ |
| description | 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 low-resource corpus. |
| first_indexed | 2024-04-10T23:47:44Z |
| format | Article |
| id | doaj.art-1f3c6be9287e42d9bce94d0e5cdf78f0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T23:47:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1f3c6be9287e42d9bce94d0e5cdf78f02023-01-11T00:00:24ZengIEEEIEEE Access2169-35362023-01-01112182219810.1109/ACCESS.2022.32294909987471A Novel Patent Knowledge Extraction Method for Innovative DesignGaofeng Yue0https://orcid.org/0000-0002-1824-6231Jihong Liu1https://orcid.org/0000-0003-2983-8766Yongzhu Hou2https://orcid.org/0000-0003-0001-6099Qiang Zhang3School of Mechanical Engineering and Automation, Beihang University, Beijing, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing, ChinaBeijing Institute of Mechanical and Electrical Engineering, Beijing, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing, ChinaAs 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 low-resource corpus.https://ieeexplore.ieee.org/document/9987471/Patent analysisdesign rationaleknowledge extractiondesign knowledge network |
| spellingShingle | Gaofeng Yue Jihong Liu Yongzhu Hou Qiang Zhang A Novel Patent Knowledge Extraction Method for Innovative Design IEEE Access Patent analysis design rationale knowledge extraction design knowledge network |
| title | A Novel Patent Knowledge Extraction Method for Innovative Design |
| title_full | A Novel Patent Knowledge Extraction Method for Innovative Design |
| title_fullStr | A Novel Patent Knowledge Extraction Method for Innovative Design |
| title_full_unstemmed | A Novel Patent Knowledge Extraction Method for Innovative Design |
| title_short | A Novel Patent Knowledge Extraction Method for Innovative Design |
| title_sort | novel patent knowledge extraction method for innovative design |
| topic | Patent analysis design rationale knowledge extraction design knowledge network |
| url | https://ieeexplore.ieee.org/document/9987471/ |
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