Constructing Knowledge Graph for Social Networks in A Deep and Holistic Way
Qi He, Jaewon Yang, Baoxu Shi
Abstract
Online social networks such as Facebook and LinkedIn have been an integrated part of everyday life. To improve the user experience and power the products around the social network, Knowledge Graphs (KG) are used as a standard way to extract and organize the knowledge in social networks. This tutorial focuses on how to build KGs for social networks by developing deep NLP models, and holistic optimization of KGs and the social network. Building KG for social networks poses two challenges: 1) input data for each member in the social network is noisy, implicit and in multilingual, so a deep understanding of the input data is needed; 2) KG and the social network influence each other via multiple organic feedback loops, so a holistic view on both networks is needed.