This paper contributes a novel embedding model which measures the probability of each belief @math in a large-scale knowledge repository via simultaneously learning distributed representations for entities ( @math and @math ), relations ( @math ), and the words in relation mentions ( @math ). It facilitates knowledge completion by means of simple vector operations to discover new beliefs. Given an imperfect belief, we can not only infer the missing entities, predict the unknown relations, but also tell the plausibility of the belief, just leveraging the learnt embeddings of remaining evidences. To demonstrate the scalability and the effectiveness of our model, we conduct experiments on several large-scale repositories which contain millions of beliefs from WordNet, Freebase and NELL, and compare it with other cutting-edge approaches via competing the performances assessed by the tasks of entity inference, relation prediction and triplet classification with respective metrics. Extensive experimental results show that the proposed model outperforms the state-of-the-arts with significant improvements.
Due to the diverse feature spaces between unstructured texts and structured beliefs, one key challenge of connecting natural language and knowledge is to project the features into the same space and to merge them together for knowledge completion. @cite_3 have recently proposed JRME to jointly learn the embedding representations for both relations and mentions in order to predict unknown relations between entities in NELL. However, the functionality of their latest method is limited to the relation prediction task (see section ), as the correlations between entities and relations are ignored. Therefore, we desire a comprehensive model that can simultaneously consider entities, relations and even the relation mentions, and can integrate the heterogeneous resources to support multiple subtasks of knowledge completion, such as entity inference , relation prediction and triplet classification .
@cite_3
1777908065
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 stand-alone approaches which separately operate on either knowledge bases or free texts. The proposed model simultaneously learns low-dimensional vector representations 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.