基于知识推理的纹饰演化关系发现

doi:10.3778/j.issn.1002-8331.2011-0118

计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (14): 194-199.DOI: 10.3778/j.issn.1002-8331.2011-0118

基于知识推理的纹饰演化关系发现

卢明，侯小刚，韩晓彤，赵海英

1.甘肃省科学院自动化研究所，兰州 730030
2.北京邮电大学人工智能学院，北京 100876
3.北京邮电大学数字媒体与设计艺术学院，北京 100876
4.北京邮电大学世纪学院移动媒体与文化计算北京市重点实验室，北京 102101

出版日期:2022-07-15 发布日期:2022-07-15

Discovery of Evolutionary Relationship of Ornaments Based on Knowledge Reasoning

LU Ming, HOU Xiaogang, HAN Xiaotong, ZHAO Haiying

1.Institute of Automation of Gansu Academy of Sciences, Lanzhou 730000, China
2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
3.School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing 100876, China
4.Beijing Key Laboratory of Mobile Media and Cultural Computing, Century College, Beijing University of Posts and Telecommunications, Beijing 102101, China

Online:2022-07-15 Published:2022-07-15

摘要/Abstract

摘要： 通过对传统纹饰的时间、空间、历史等诸多要素的梳理和分析，挖掘其时空演化规律，并探寻一套传承纹饰文化内涵的脉络。提取传统纹饰的视觉特征，以视觉相似度作为纹饰相关度的度量值，并依据纹饰相关属性构建传统纹饰语义网。利用所提的基于知识推理的演化关系发现算法预测传统纹饰的演化过程。以《中国纹样全集》为实验数据，抽取纹饰属性及其关系，整理出不同时期不同地域的纹饰共有1?500个，纹饰之间形成的关系有13?600个，通过实验验证了基于知识推理的传统纹样演化关系发现算法的可行性，并通过可视化呈现更直观地展示了纹饰的演化过程。

关键词: 传统纹饰, 演化关系发现, 纹饰语义网, 纹饰相关度

Abstract: Through sorting out and analyzing the element in time, space, history and other elements of traditional ornamentation, this paper exploits the rule of its spatio-temporal evolution, and explores a set of veins for inheriting the cultural connotation of ornamentation. In this paper, visual features of traditional ornamentation are firstly extracted, and the visual similarity is taken as the measurement value of the relevancy of ornamentation, and the semantic network of traditional ornamentation is constructed according to the related attributes of ornamentation. Then the evolutionary process of traditional ornamentation is predicted by the evolutionary relationship discovery algorithm based on knowledge reasoning. Finally, taking the Complete Set of Chinese Patterns as experimental data, the attributes and relationships of patterns are extracted, and a total of 1 500 patterns in different periods and different regions are sorted out, with 13 600 relationships formed between patterns. Through experiments, the feasibility of the traditional algorithm based on knowledge reasoning to discover the evolution relationship of patterns is verified, and the evolution process of patterns is shown more intuitively through visual presentation.

Key words: traditional ornamentation, discovery of evolutionary relations, decorative semantic network, relevance of decorative

卢明, 侯小刚, 韩晓彤, 赵海英. 基于知识推理的纹饰演化关系发现[J]. 计算机工程与应用, 2022, 58(14): 194-199.

LU Ming, HOU Xiaogang, HAN Xiaotong, ZHAO Haiying. Discovery of Evolutionary Relationship of Ornaments Based on Knowledge Reasoning[J]. Computer Engineering and Applications, 2022, 58(14): 194-199.

参考文献

[1] 赵海英，陈洪，贾耕云，等.基于字典学习的民族文化图案语义标注[J].中国科学：信息科学，2019，49（2）：172-187.
ZHAO H Y，CHEN H，JIA G Y，et al.Semantic annotation of national cultural patterns based on dictionary learning[J].SCIENTIA SINICA：Informationis，2019，49（2）：172-187.
[2] 侯小刚.传统文化图案分割关键技术研究[D].北京：北京邮电大学，2021.
HOU X G.Research on the key technology of traditional culture patterns segmentation[D].Beijing：Beijing University of Posts and Telecommunications，2021.
[3] 赵海英，周伟，侯小刚，等.基于多任务学习的传统服饰图像双层标注[J].吉林大学学报（工学版），2021，51（1）：293-302.
ZHAO H Y，ZHOU W，HOU X G，et al.Double-layer annotation of traditional costume images based on multi-task learning[J].Journal of Jilin University（Engineering and Technology Edition），2021，51（1）：293-302.
[4] 赵海英，周伟，侯小刚，等.多标签分类的传统民族服饰纹样图像语义理解[J].光学精密工程，2020，28（3）：695-703.
ZHAO H Y，ZHOU W，HOU X G，et al.Multi-label classification of traditional national costume pattern image semantic understanding[J].Optics and Precision Engineering，2020，28（3）：695-703.
[5] RICHARDSON M，DOMINGOS P.Markov logic networks[J].Machine Learning，2006，63（2）：207.
[6] FADJA A N，RIGUZZI F.Lifted discriminative learning of probabilistic logic programs[J].Machine Learning，2019，108：1111-1135.
[7] SCHOENMACKERS S，DAVIS J，ETZIONI O，et al.Learning first-order horn clauses from web text[C]//Conference on Empirical Methods in Natural Language Processing，2010：1088-1098.
[8] LAO N，MITCHELL T，COHEN W W.Random walk inference and learning in a large scale knowledge base[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing，Edinburgh，UK，2011：529-539.
[9] GARDNER M，MITCHELL T.Efficient and expressive knowledge base completion using subgraph feature extraction[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，Lisbon，Portuga，2015：1488-1498.
[10] WANG Z，LI J.RDF2Rules：learning rules from RDF knowledge bases by mining frequent predicate cycles[J].arXiv：1512.07734，2015.
[11] GALáRRAGA L A，TEFLIOUDI C，HOSE K，et al.AMIE：association rule mining under incomplete evidence in ontological knowledge bases[C]//Proceedings of the 22nd International Conference on World Wide Web，2013：413-422.
[12] GALáRRAGA L，TEFLIOUDI C，HOSE K，et al.Fast rule mining in ontological knowledge bases with AMIE+[J].The VLDB Journal，2015，24（6）：707-730.
[13] MIKOLOV T，SUTSKEVER I，CHEN K，et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems，Lake Tahoe，Nevada，2013：3111-3119.
[14] MIKOLOV T，CHEN K，CORRADO G，et al.Efficient estimation of word representations in vector space[C]//1st International Conference on Learning Representations，Scottsdale，AZ，United States，2013：1-10.
[15] BORDES A，USUNIER N，GARCIA-DURAN A，et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems，Lake Tahoe，NV，United States，2013：2787-2795.
[16] WANG Z，ZHANG J，FENG J，et al.Knowledge graph embedding by translating on hyperplanes[C]//Twenty-Eighth AAAI Conference on Artificial Intelligence，Quebec City，Canada，2014：1112-1119.
[17] XIAO H，HUANG M，YU H，et al.Trans G：a generative mixture model for knowledge graph embedding[C]//Proceedings of Annual Meeting of the Association for Computational Linguistics，Berlin，Germany，2016：2316-2325.
[18] SOCHER R，CHEN D，MANNING C，et al.Reasoning with neural tensor networks for knowledge base completion[C]//International Conference on Neural Information Processing Systems，Lake Tahoe，Nevada，USA，2013：926-934.
[19] XIE R，LIU Z，JIA J，et al.Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of Thirtieth AAAI Conference on Artificial Intelligence，Phoenix，Arizona，USA，2016：2659-2665.
[20] NEELAKANTAN A，ROTH B，MC CALLUM A.Compositional vector space models for knowledge base inference[C]//Proceedings of 2015 AAAI Spring Symposium Series，Palo Alto，California，USA，2015：1-16.
[21] 安宁.一类传统纹饰演化关系发现与可视化研究[D].北京邮电大学，2020.
AN N.Evolutionary relationship discovery and visualization of typical traditional decoration[D].Beijing：Beijing University of Posts and Telecommunications，2020.

基于知识推理的纹饰演化关系发现

Discovery of Evolutionary Relationship of Ornaments Based on Knowledge Reasoning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics