论文标题
消息传递查询嵌入
Message Passing Query Embedding
论文作者
论文摘要
关于知识图的表示图表的最新著作已超越了链接预测的问题，回答了任意结构的查询。现有方法基于需要培训的临时机制，这些机制具有多种查询结构。我们提出了一个更通用的体系结构，该体系结构采用图神经网络来编码查询的图表表示，其中节点对应于实体和变量。我们方法的通用性使其与以前的工作相比，它可以编码一组更多样化的查询类型。我们的方法显示了与以前的复杂查询模型相对于以前的模型的竞争性能，并且与这些模型相反，仅在培训链接预测时，它可以回答复杂的查询。我们表明，该模型学习实体嵌入，可以在没有明确监督的情况下捕获实体类型的概念。
Recent works on representation learning for Knowledge Graphs have moved beyond the problem of link prediction, to answering queries of an arbitrary structure. Existing methods are based on ad-hoc mechanisms that require training with a diverse set of query structures. We propose a more general architecture that employs a graph neural network to encode a graph representation of the query, where nodes correspond to entities and variables. The generality of our method allows it to encode a more diverse set of query types in comparison to previous work. Our method shows competitive performance against previous models for complex queries, and in contrast with these models, it can answer complex queries when trained for link prediction only. We show that the model learns entity embeddings that capture the notion of entity type without explicit supervision.
