Richpedia: A Large-Scale, Comprehensive Multi-Modal Knowledge Graph

被引:121
作者
Wang, Meng [1 ,2 ]
Wang, Haofen [3 ]
Qi, Guilin [1 ,2 ]
Zheng, Qiushuo [4 ]
机构
[1] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Peoples R China
[3] Tongji Univ, Intelligent Big Data Visualizat Lab, Shanghai, Peoples R China
[4] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
Knowledge graph; Multi-modal; Wikidata; Ontology; LANGUAGE;
D O I
10.1016/j.bdr.2020.100159
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation, while paying little attention to the proliferation of visual resources on the Web. To consolidate this recent trend, in this paper, we present Richpedia, aiming to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set Resource Description Framework links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint, which provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页数:11
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