Jointly Embedding Relations and Mentions for Knowledge Population

摘要

This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of relation inference. It differs from most standalone approaches which separately operate on either knowledge bases or free texts. The proposed model simultaneously learn-s low-dimensional vector representation-s for both triplets in knowledge repositories and the mentions of relations in free texts, so that we can leverage the evidence both resources to make more accurate predictions. We use NELL to evaluate the performance of our approach, compared with cutting-edge methods. Results of extensive experiments show that our model achieves significant improvement on relation extraction.