@inproceedings{li-cheng-2019-divine,
title = "{DIVINE}: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning",
author = "Li, Ruiping and
Cheng, Xiang",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1266",
doi = "10.18653/v1/D19-1266",
pages = "2642--2651",
abstract = "Knowledge graphs (KGs) often suffer from sparseness and incompleteness. Knowledge graph reasoning provides a feasible way to address such problems. Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance. However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs. To this end, in this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods. DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods while eliminating extra reward engineering.",
}
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<abstract>Knowledge graphs (KGs) often suffer from sparseness and incompleteness. Knowledge graph reasoning provides a feasible way to address such problems. Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance. However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs. To this end, in this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods. DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods while eliminating extra reward engineering.</abstract>
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%0 Conference Proceedings
%T DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
%A Li, Ruiping
%A Cheng, Xiang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-cheng-2019-divine
%X Knowledge graphs (KGs) often suffer from sparseness and incompleteness. Knowledge graph reasoning provides a feasible way to address such problems. Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance. However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs. To this end, in this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods. DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods while eliminating extra reward engineering.
%R 10.18653/v1/D19-1266
%U https://aclanthology.org/D19-1266
%U https://doi.org/10.18653/v1/D19-1266
%P 2642-2651
Markdown (Informal)
[DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning](https://aclanthology.org/D19-1266) (Li & Cheng, EMNLP-IJCNLP 2019)
ACL