  We study the extent to which online social networks can be connected to open
knowledge bases. The problem is referred to as learning social knowledge
graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn
latent topics that generate word and network embeddings. GenVector leverages
large-scale unlabeled data with embeddings and represents data of two
modalities---i.e., social network users and knowledge concepts---in a shared
latent topic space. Experiments on three datasets show that the proposed method
clearly outperforms state-of-the-art methods. We then deploy the method on
AMiner, a large-scale online academic search system with a network of
38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our
method significantly decreases the error rate in an online A/B test with live
users.
