Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors
Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305, USA
Abstract
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 75.8%.
中文速览
知识库（knowledge base）如 WordNet 往往不完整，如何自动补全其中实体之间缺失的关系是个长期难题。研究者提出了一种神经张量网络（Neural Tensor Network, NTN），通过张量双线性层让两个实体向量直接交互，从而比传统神经网络层更有效地捕捉实体间的关系模式。在此基础上，他们还引入了从维基百科无监督学习得到的词向量来初始化实体表示，使模型甚至能对原本不在知识库中的实体进行关系预测。实验表明，NTN 在 WordNet 上对未见关系的分类准确率达到 75.8%，远超此前同类模型，且在从近四万个实体中排名正确答案的任务上同样大幅领先。这项工作意义在于，仅凭知识库自身的结构信息（加上可选的无标注文本），就能显著扩充知识库，为问答、信息检索等下游应用提供更丰富可靠的结构化知识。
原文 arXiv:1301.3618;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1301.3618v2