Embedding Entities and Relations for Learning and Inference in Knowledge Bases
This work addresses the challenge of improving link prediction and rule mining in knowledge bases, which is crucial for AI applications like semantic search and reasoning, but it is incremental as it builds on existing neural-embedding frameworks.
The paper tackles the problem of learning representations for entities and relations in knowledge bases, showing that a simple bilinear formulation achieves state-of-the-art results with a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase, and introduces a novel embedding-based approach for mining logical rules that outperforms a confidence-based method.
We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.