Constructing and Mining Web-Scale Knowledge Graphs

被引:22
作者
Bordes, Antoine [1 ]
Gabrilovich, Evgeniy [2 ]
机构
[1] Facebook, 770 Broadway, New York, NY 10003 USA
[2] Google, Mountain View, CA 94043 USA
来源
PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14) | 2014年
关键词
D O I
10.1145/2623330.2630803
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recent years have witnessed a proliferation of large-scale knowledge graphs, such as Freebase, YAGO, Google's Knowledge Graph, and Microsoft's Satori. Whereas there is a large body of research on mining homogeneous graphs, this new generation of information networks are highly heterogeneous, with thousands of entity and relation types and billions of instances of vertices and edges. In this tutorial, we will present the state of the art in constructing, mining, and growing knowledge graphs. The purpose of the tutorial is to equip newcomers to this exciting field with an understanding of the basic concepts, tools and methodologies, available datasets, and open research challenges. A publicly available knowledge base (Freebase) will be used throughout the tutorial to exemplify the different techniques.
引用
收藏
页码:1967 / 1967
页数:1
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