Research Progress of Knowledge Graph Embedding

Duan, Chengyuan; You, Heyuan; Cai, Zhiping; Zhang, Ziwei

doi:10.1007/978-981-15-8083-3_17

Cited by 1 publication

(1 citation statement)

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“…In recent years, as a semantic network, knowledge graph has been widely used in natural language processing [46], intelligent question-answering systems [47], intelligent recommendation systems [48] and other fields. Knowledge graph, together with big data and deep learning, has become the core driving force for artificial intelligence development [49,50]. The topic distribution characteristics and evolution rules can be revealed through the knowledge graph, which promotes knowledge mining and discovery.…”

Section: Knowledge Graph-based Topic Maps and Gtmentioning

confidence: 99%

Exploring the topic evolution of Dunhuang murals through image classification

Zeng

Sun

et al. 2022

Journal of Information Science

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Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k-means clustering algorithm, and the term frequency–inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed.

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