Abstract:
In statistical relational learning, knowledge base completion deals with automatically understanding the structure of large knowledge bases—labeled directed graphs—and predicting missing relationships—labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices—thus all possible relation/adjacency matrices—are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks. Bio:
After graduating from ENSIMAG, Théo started his PhD at Univ. Grenoble Alpes and at Xerox Research Centre Europe. He is currently visiting PhD student in the UCL Machine Reading team. His main research topic is statistical relational learning, focusing on complex-valued embedding models. |