Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor do...Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.展开更多
At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production ...At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.展开更多
在深度强化学习方法中,针对内在好奇心模块(intrinsic curiosity model,ICM)指导智能体在稀疏奖励环境中获得未知策略学习的机会,但好奇心奖励是一个状态差异值,会使智能体过度关注于对新状态的探索,进而出现盲目探索的问题,提出了一种...在深度强化学习方法中,针对内在好奇心模块(intrinsic curiosity model,ICM)指导智能体在稀疏奖励环境中获得未知策略学习的机会,但好奇心奖励是一个状态差异值,会使智能体过度关注于对新状态的探索,进而出现盲目探索的问题,提出了一种基于知识蒸馏的内在好奇心改进算法(intrinsic curiosity model algorithm based on knowledge distillation,KD-ICM)。首先,该算法引入知识蒸馏的方法,使智能体在较短的时间内获得更丰富的环境信息和策略知识,加速学习过程;其次,通过预训练教师神经网络模型去引导前向网络,得到更高精度和性能的前向网络模型,减少智能体的盲目探索。在Unity仿真平台上设计了两个不同的仿真实验进行对比,实验表明,在复杂仿真任务环境中,KD-ICM算法的平均奖励比ICM提升了136%,最优动作概率比ICM提升了13.47%,提升智能体探索性能的同时能提高探索的质量,验证了算法的可行性。展开更多
基金This study was funded by the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province,China(No.2021KW-16)the Science and Technology Project in Xi’an(No.2019218114GXRC017CG018-GXYD17.11),Thesis work was supported by the special fund construction project of Key Disciplines in Ordinary Colleges and Universities in Shaanxi Province,the authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
文摘Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.
基金supported by the Sichuan Science and Technology Program under Grants No.2022YFQ0052 and No.2021YFQ0009.
文摘At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.
文摘在深度强化学习方法中,针对内在好奇心模块(intrinsic curiosity model,ICM)指导智能体在稀疏奖励环境中获得未知策略学习的机会,但好奇心奖励是一个状态差异值,会使智能体过度关注于对新状态的探索,进而出现盲目探索的问题,提出了一种基于知识蒸馏的内在好奇心改进算法(intrinsic curiosity model algorithm based on knowledge distillation,KD-ICM)。首先,该算法引入知识蒸馏的方法,使智能体在较短的时间内获得更丰富的环境信息和策略知识,加速学习过程;其次,通过预训练教师神经网络模型去引导前向网络,得到更高精度和性能的前向网络模型,减少智能体的盲目探索。在Unity仿真平台上设计了两个不同的仿真实验进行对比,实验表明,在复杂仿真任务环境中,KD-ICM算法的平均奖励比ICM提升了136%,最优动作概率比ICM提升了13.47%,提升智能体探索性能的同时能提高探索的质量,验证了算法的可行性。
