Two-stage wheat knowledge graph completion method based on large language models rule augmentation and channel attention

A knowledge graph is often required to collect and integrate the vast amount of data with the rapid development of agricultural informatization in recent years. But the data can be characterized by dispersal and fragmentation. Relevant data is usually stored separately in different institutions and databases, leading to data sparsity and missing information in the agricultural knowledge system. Among them, knowledge graph completion can be used to extract the potential semantic information using a neural network. The domain-specific data features can also be obtained after special domain knowledge graph applications using convolution and pooling operations. However, existing knowledge completion models still struggle to effectively capture the long-range dependencies in the complex relational scenarios. In this study, a two-stage knowledge graph completion model (RASE-ARKGC) was proposed using large language models with rule augmentation and channel attention. The wheat was taken as an example. In the first stage, the large language models (LLMs) were used for the rule augmentation (RA). In particular, both the semantic and structural information of the knowledge graph were utilized to generate the logical rules. Firstly, the paths were sampled from the knowledge graph to represent the structural information. Secondly, the rule generator was adopted to mine the potential rules using semantic and structural information. Thirdly, the logical rule sequence was used to evaluate the quality of the rules and then filter out the outliers. As such, the rule augmentation module effectively expanded the dataset in order to alleviate the data sparsity in the knowledge graph. The channel attention mechanism and dilated convolution (SE-ARKGC) were introduced in the second stage. Specifically, the channel attention mechanism was enhanced to capture the long-range dependencies. While the dilated convolution was used to adjust the dilation rate for the multi-scale receptive fields, in order to capture the multi-level interactions between entities and relationships. In addition, the residual module was also used to effectively mitigate the gradient vanishing in the deep network, in order to further enhance the expression of the model. The complete transfer of feature information was then realized in the multilayer network. A series of experiments was conducted on the self-constructed wheat knowledge graph (WheatSeedBiz) in order to validate the effectiveness of the two-stage knowledge graph completion. The results showed that the RASE-ARKGC model achieved 0.482 in MRR and 0.555 in Hits@10, which were improved by 9.8% and 10.2%, respectively, compared with the ConvE model. In addition, the experiments were conducted on the open-source datasets WN18 and FB15k to evaluate the generalization of the SE-ARKGC model. The experimental results showed that the optimal or sub-optimal performance was achieved over multiple baseline models. Ablation experiments were also carried out to evaluate the rule augmentation and the SE-Block-based knowledge graph completion. Each module was enhanced the performance of the model. Overall, the RASE-ARKGC model effectively expanded the dataset for optimal performance in the knowledge completion task. The finding can provide an improved approach for the knowledge graph completion in the wheat domain. The generalization of the model can be applicable to the rest domains.