Open AccessProceedings Article
Multi-modal Bayesian embeddings for learning social knowledge graphs
Zhilin Yang,Jie Tang,William W. Cohen +2 more
- pp 2287-2293
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TLDR
A multi-modal Bayesian embedding model, GenVector, is proposed to learn latent topics that generate word and network embeddings in a shared latent topic space, and significantly decreases the error rate in an online A/B test with live users.Abstract:
We study the extent to which online social networks can be connected to 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 embeddings and network embeddings simultaneously. 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, an online academic search system to connect with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate of learning social knowledge graphs in an online A/B test with live users.read more
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