Quantitative Mining and Genealogy Construction of Aesthetic Features of Ancient Chinese Porcelain Patterns Driven by Big Data.
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| Title: | Quantitative Mining and Genealogy Construction of Aesthetic Features of Ancient Chinese Porcelain Patterns Driven by Big Data. |
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| Authors: | XIAO, Xiao1 xiaoxiao_whu@163.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1233-1243. 11p. |
| Subjects: | Chinese porcelain, Aesthetics, Machine learning, Big data, Data analysis, Knowledge graphs |
| Abstract: | Porcelain, as a precious heritage in the treasure house of Chinese civilization, has become a key technology for promoting its protection and inheritance under the trend of digitalization and intelligence of cultural heritage. To improve the matching accuracy between the visual aesthetic features of ancient Chinese porcelain patterns and the corresponding textual information, this paper proposes a cross-modal entity alignment strategy based on pattern aesthetics within the visual-language pre-training framework. On this basis, a porcelain cultural knowledge graph platform based on big data is designed and developed, which integrates multiple core functional modules such as intelligent interactive questioning, speech synthesis, pattern aesthetic text generation, entity recognition, and relationship extraction. The actual system operation test shows that the average recognition accuracy exceeds 90% and the average response time is less than 5 seconds, verifying the efficiency and reliability of the system in practical applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131810 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quantitative Mining and Genealogy Construction of Aesthetic Features of Ancient Chinese Porcelain Patterns Driven by Big Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22XIAO%2C+Xiao%22">XIAO, Xiao</searchLink><relatesTo>1</relatesTo><i> xiaoxiao_whu@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1233-1243. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Chinese+porcelain%22">Chinese porcelain</searchLink><br /><searchLink fieldCode="DE" term="%22Aesthetics%22">Aesthetics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Porcelain, as a precious heritage in the treasure house of Chinese civilization, has become a key technology for promoting its protection and inheritance under the trend of digitalization and intelligence of cultural heritage. To improve the matching accuracy between the visual aesthetic features of ancient Chinese porcelain patterns and the corresponding textual information, this paper proposes a cross-modal entity alignment strategy based on pattern aesthetics within the visual-language pre-training framework. On this basis, a porcelain cultural knowledge graph platform based on big data is designed and developed, which integrates multiple core functional modules such as intelligent interactive questioning, speech synthesis, pattern aesthetic text generation, entity recognition, and relationship extraction. The actual system operation test shows that the average recognition accuracy exceeds 90% and the average response time is less than 5 seconds, verifying the efficiency and reliability of the system in practical applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20251203003184 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1233 Subjects: – SubjectFull: Chinese porcelain Type: general – SubjectFull: Aesthetics Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Big data Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Knowledge graphs Type: general Titles: – TitleFull: Quantitative Mining and Genealogy Construction of Aesthetic Features of Ancient Chinese Porcelain Patterns Driven by Big Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: XIAO, Xiao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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