论文标题
序列到序列知识图的完成和问答答案
Sequence-to-Sequence Knowledge Graph Completion and Question Answering
论文作者
论文摘要
知识图嵌入（KGE）模型代表具有低维嵌入向量的知识图（KG）的每个实体和关系。这些方法最近已应用于kg链接预测和对不完整KGS（KGQA）的问题。 KGES通常为图中的每个实体创建一个嵌入式，这会在具有数百万个实体的现实图表上产生较大的模型大小。对于下游任务，这些原子实体表示通常需要集成到多阶段管道中，从而限制了它们的效用。我们表明，现成的编码器码头变压器模型可以用作可扩展且通用的KGE模型，可为KG链接预测和不完整的kg问题回答获得最新的结果。我们通过将kg链接预测作为序列到序列任务提出，并交换了先前的KGE方法和自回解码的三分法方法。与传统的KGE模型相比，这种简单但功能强大的方法将模型大小降低到98％，同时保持推理时间可拖延。在对KGQA在不完整的KG上的任务上进行了填充模型之后，我们的方法在没有广泛的超参数调整的情况下优于多个大型数据集上的基准。
Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA). KGEs typically create an embedding for each entity in the graph, which results in large model sizes on real-world graphs with millions of entities. For downstream tasks these atomic entity representations often need to be integrated into a multi stage pipeline, limiting their utility. We show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and incomplete KG question answering. We achieve this by posing KG link prediction as a sequence-to-sequence task and exchange the triple scoring approach taken by prior KGE methods with autoregressive decoding. Such a simple but powerful method reduces the model size up to 98% compared to conventional KGE models while keeping inference time tractable. After finetuning this model on the task of KGQA over incomplete KGs, our approach outperforms baselines on multiple large-scale datasets without extensive hyperparameter tuning.
