We present a scalable probabilistic framework for learning from multi-relational data, given in form of entity-relation-entity triplets, with a potentially massive number of entities and relations (e.g., in multirelational networks, knowledge bases, etc.). We dene each triplet via a relation-specic bilinear function of the embeddings of entities associated with it (these embeddings correspond to \topics"). To handle massive number of relations and the data sparsity problem (very few observations per relation), we also extend this model to allow sharing of parameters across relations, which leads to a substantial reduction in the number of parameters to be learned. In addition to yielding excellent predictive performance (e.g., for knowledge base completion tasks), the interpretability of our topic-based embedding framework enables easy qualitative analyses. Computational cost of our models scales in the number of positive triplets, which makes it easy to scale to massive realworld multi-relational data sets, which are usually extremely sparse. We develop simpleto-implement batch as well as online Gibbs sampling algorithms and demonstrate the effectiveness of our models on tasks such as multi-relational link-prediction, and learning from large knowledge bases.
['Changwei Hu', 'Piyush Rai', 'Lawrence Carin']
Topic-Based Embeddings for Learning from Large Knowledge Graphs