Analogical Inference for Multi-Relational Embeddings
This work addresses scalable knowledge-based inference for applications like recommendation systems, but it is incremental as it builds on existing multi-relational embedding methods.
The paper tackles the problem of learning latent representations for entities and relations in large knowledge graphs by proposing a novel framework that optimizes for analogical properties, resulting in significant performance improvements over baseline methods on benchmark datasets.
Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework.