论文标题
简单问题回答知识图的神经关系预测
Neural Relation Prediction for Simple Question Answering over Knowledge Graph
论文作者
论文摘要
知识图被广泛用作典型资源，以提供Factoid问题的答案。在简单的问题上回答知识图，关系提取旨在从一组预定义的关系类型中预测事实问题的关系。最新方法利用神经网络将问题与所有预定义关系匹配。在本文中，我们提出了一种基于实例的方法来捕获问题的潜在关系，并为此目的检测到一个共享相同关系的新问题的匹配措辞，并选择了它们的相应关系作为我们的预测。我们的模型根源的想法是，可以用各种形式的问题表示关系，而这些形式在词汇或语义上相似的术语和概念共享。我们在简单问题数据集上的实验表明，与最先进的关系提取模型相比，所提出的模型的准确性更好。
Knowledge graphs are widely used as a typical resource to provide answers to factoid questions. In simple question answering over knowledge graphs, relation extraction aims to predict the relation of a factoid question from a set of predefined relation types. Most recent methods take advantage of neural networks to match a question with all predefined relations. In this paper, we propose an instance-based method to capture the underlying relation of question and to this aim, we detect matching paraphrases of a new question which share the same relation, and their corresponding relation is selected as our prediction. The idea of our model roots in the fact that a relation can be expressed with various forms of questions while these forms share lexically or semantically similar terms and concepts. Our experiments on the SimpleQuestions dataset show that the proposed model achieves better accuracy compared to the state-of-the-art relation extraction models.
