@inproceedings{lei-etal-2020-learning,
title = "Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning",
author = "Lei, Deren and
Jiang, Gangrong and
Gu, Xiaotao and
Sun, Kexuan and
Mao, Yuning and
Ren, Xiang",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.688",
doi = "10.18653/v1/2020.emnlp-main.688",
pages = "8541--8547",
abstract = "Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.",
}
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<abstract>Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.</abstract>
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%0 Conference Proceedings
%T Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
%A Lei, Deren
%A Jiang, Gangrong
%A Gu, Xiaotao
%A Sun, Kexuan
%A Mao, Yuning
%A Ren, Xiang
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lei-etal-2020-learning
%X Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.
%R 10.18653/v1/2020.emnlp-main.688
%U https://aclanthology.org/2020.emnlp-main.688
%U https://doi.org/10.18653/v1/2020.emnlp-main.688
%P 8541-8547
Markdown (Informal)
[Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning](https://aclanthology.org/2020.emnlp-main.688) (Lei et al., EMNLP 2020)
ACL