论文标题
用双向序列编码在知识图中回答复杂查询
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
论文作者
论文摘要
知识图（KGS）的表示学习集中在回答简单链接预测查询的问题上。在这项工作中，我们解决了与多个缺失实体相结合查询的答案的更雄心勃勃的挑战。我们提出了双向查询嵌入（BIQE），该方法将基于双向注意机制的模型嵌入连接性查询。与先前的工作相反，双向自我注意可以捕获查询图的所有元素之间的相互作用。我们介绍了一个新的数据集，以预测连接性查询的答案，并进行实验，以表明BIQE明显超过了艺术基线的状态。
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of predicting the answers of conjunctive queries with multiple missing entities. We propose Bi-Directional Query Embedding (BIQE), a method that embeds conjunctive queries with models based on bi-directional attention mechanisms. Contrary to prior work, bidirectional self-attention can capture interactions among all the elements of a query graph. We introduce a new dataset for predicting the answer of conjunctive query and conduct experiments that show BIQE significantly outperforming state of the art baselines.
