论文标题
开放世界知识图完成的基于概率的案例推理
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
论文作者
论文摘要
基于案例的推理（CBR）系统通过检索与给定问题的“案例”来解决一个新问题。如果这样的系统能够达到高精度，则由于其简单性，可解释性和可伸缩性而吸引它。在本文中，我们证明了在知识基础（KB）中推理的这种系统是可以实现的。我们的方法通过从KB中的类似实体收集推理路径来预测实体的属性。我们的概率模型估计道路有效地回答有关实体的查询的可能性。我们的模型参数可以使用简单的路径统计信息有效地计算，并且不需要迭代优化。我们的模型是非参数，随着新实体和关系添加到KB中，动态增长。在几个基准数据集上，我们的方法极大地胜过其他规则学习方法，并与基于最新的嵌入方式相当地执行。此外，我们在“开放世界”设置中演示了模型的有效性，新实体以在线方式到达，大大优于最先进的方法，并且几乎匹配了最佳的离线方法。可在https://github.com/ameyagodbole/prob-cbr上找到代码
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an "open-world" setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method. Code available at https://github.com/ameyagodbole/Prob-CBR
