首页> 外文会议>International Joint Conference on Natural Language Processing;Annual Meeting of the Association for Computational Linguistics >Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
【24h】

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

机译:从历史和未来的原因搜索:时间知识图中的两级推理

获取原文

摘要

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage. CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.
机译:时间知识图(TKGS)已经开发并用于许多不同的区域。在未来预测潜在事实(事件)的TKG的推理带来了对现有模型的巨大挑战。当面对预测任务时,人类通常在他们的记忆中搜索有用的历史信息(即,线索),然后是精心的理由。受到这种机制的启发,我们提出了以两阶段的方式预测未来事实,因此线索搜索和时间推理。具体地,在线索搜索阶段。群集通过强化学习(RL)来学习光束搜索策略,以诱导历史事实的多个线索。在时间推理阶段,它采用了基于图形卷积网络的序列方法来推导来自线索的答案。四个数据集的实验证明集群与最先进的方法相比的实质优势。此外,群集发现的线索进一步为结果提供了解释性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号