计算机科学与探索2024，Vol.18Issue(6)：1590-1599,10.DOI:10.3778/j.issn.1673-9418.2308085
多模态课程学习知识图谱实体预测方法研究
Research on Knowledge Graph Entity Prediction Method of Multi-modal Curriculum Learning
摘要
Abstract
On the one hand,the existing knowledge graph entity prediction methods only use the neighborhood and graph structure information to enhance the node information,and ignore the multi-modal information outside the knowledge graph to enhance the knowledge graph information.On the other hand,when comparing positive and negative samples to train the model,the negative sample random ordering results in poor training effect,and there is no additional information to help the training process of negative samples.Therefore,a multi-modal curriculum learning knowledge graph entity prediction model(MMCL)is proposed.Firstly,multi-modal information is intro-duced into the knowledge graph to achieve information enhancement,and the multi-modal information fusion pro-cess is optimized using generative adversarial network(GAN).The samples generated by the generator enhance the knowledge graph information,and at the same time improve the discriminator's ability to distinguish the truth and falsity of triples.Secondly,the course learning algorithm is used to sort the negative samples from easy to difficult according to the difficulty of the negative samples.By adding the sorted negative samples into the training process hierarchically through the pace function,it is more beneficial to playing the effect of negative samples in identifying the truth and falseness of triples,and at the same time,no label learning avoids the false-negative problem in the late training period.The discriminators share parameters with course learning training models to help improve the train-ing effect of negative samples.Experiments are conducted on two datasets,FB15k-237 and WN18RR.The results show that compared with the baseline model,MMCL is significantly improved in mean reciprocal rank(MRR),Hits@1,Hits@3 and Hits@10.The validity and feasibility of the proposed model are verified.关键词
课程学习/多模态/生成对抗网络(GAN)/负采样Key words
curriculum learning/multi-modal/generative adversarial network(GAN)/negative sample分类
信息技术与安全科学引用本文复制引用
许智宏,郝雪梅,王利琴,董永峰,王旭..多模态课程学习知识图谱实体预测方法研究[J].计算机科学与探索,2024,18(6):1590-1599,10.基金项目
河北省高等学校科学技术研究项目(ZD2022082) （ZD2022082）
中国高等教育学会2022年度高等教育科学研究规划课题(22XX0401) （22XX0401）
河北省高等教育教学改革研究与实践项目(2022GJJG049).This work was supported by the Science and Technology Research Project of Colleges of Hebei Province(ZD2022082),the Higher Education Association 2022 Higher Education Scientific Research Planning Project of China(22XX0401),and the Teaching Reform Research and Practice Project of Hebei Province(2022GJJG049). （2022GJJG049）
