Research on Knowledge Graph Entity Prediction Method of Multi-modal Curriculum Learning

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 introduced into the knowledge graph to achieve information enhancement, and the multi-modal information fusion process 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 training 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.

Keywords

• curriculum learning; multi-modal; generative adversarial network (gan); negative sample