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Cunxiang Xie, Limin Zhang and Zhaogen Zhong
In practical application, there are different knowledge graphs in different fields, such as financial graph, commodity graph, medical graph, and so on. Entity alignment technique can be applied to the fusion of multiple knowledge graphs in different doma...
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Sirui Shen, Daobin Zhang, Shuchao Li, Pengcheng Dong, Qing Liu, Xiaoyu Li and Zequn Zhang
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However...
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Georgia Eirini Trouli, Alexandros Pappas, Georgia Troullinou, Lefteris Koumakis, Nikos Papadakis and Haridimos Kondylakis
Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary fo...
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Xiaochun Sun, Chenmou Wu and Shuqun Yang
With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in com...
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Huansha Wang, Qinrang Liu, Ruiyang Huang and Jianpeng Zhang
Multi-modal entity alignment refers to identifying equivalent entities between two different multi-modal knowledge graphs that consist of multi-modal information such as structural triples and descriptive images. Most previous multi-modal entity alignmen...
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