Learning Multi-Relational Semantics Using Neural-Embedding Models
This work addresses knowledge base completion for AI applications, but it is incremental as it builds on existing embedding models with minor improvements.
The paper tackles the problem of modeling multi-relational semantics by presenting a unified framework and empirically studying embedding models, resulting in a simple model that achieves new state-of-the-art performance on a knowledge base completion task using Freebase.
In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework. We investigate the different choices of relation operators based on linear and bilinear transformations, and also the effects of entity representations by incorporating unsupervised vectors pre-trained on extra textual resources. Our results show several interesting findings, enabling the design of a simple embedding model that achieves the new state-of-the-art performance on a popular knowledge base completion task evaluated on Freebase.