2 research outputs found
Probabilistic Belief Embedding for Knowledge Base Completion
This paper contributes a novel embedding model which measures the probability
of each belief in a large-scale knowledge repository
via simultaneously learning distributed representations for entities ( and
), relations (), and the words in relation mentions (). 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.Comment: arXiv admin note: text overlap with arXiv:1503.0815
Jointly Embedding Relations and Mentions for Knowledge Population
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