Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 82.8%.

本研究提出了一种基于神经张量网络模型（NTN）的通用数据关系预测方法，通过对给定知识库中的潜在关系进行分析，预测可能存在的新数据关系，初始实体表示使用文本中被无监督学习得到的词向量，该模型准确率可达75.8%。

利用神经张量网络和语义词向量从知识库中学习新事实