论文标题
常识知识图形推理按选择或一代？为什么？
Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?
论文作者
论文摘要
常识知识图推理（CKGR）是预测给定一个现有实体的缺失实体以及常识知识图（CKG）中的关系的任务。现有方法可以分为两类生成方法和选择方法。每种方法都有自己的优势。我们从理论和经验上比较了这两种方法，发现选择方法比CKGR中的生成方法更合适。鉴于观察结果，我们进一步结合了神经文本编码器和知识图嵌入模型的结构，以解决选择方法的两个问题，从而实现竞争结果。我们通过选择方法为后续的CKGR任务提供了基本框架和基线模型。
Commonsense knowledge graph reasoning(CKGR) is the task of predicting a missing entity given one existing and the relation in a commonsense knowledge graph (CKG). Existing methods can be classified into two categories generation method and selection method. Each method has its own advantage. We theoretically and empirically compare the two methods, finding the selection method is more suitable than the generation method in CKGR. Given the observation, we further combine the structure of neural Text Encoder and Knowledge Graph Embedding models to solve the selection method's two problems, achieving competitive results. We provide a basic framework and baseline model for subsequent CKGR tasks by selection methods.
