Abstract: [Purpose/Significance] In the era of big data, the volume of data is growing at an exponential rate. One of the most prominent areas affected by this growth is the field of agriculture. The use of agricultural knowledge graphs, which serve as key infrastructures for managing agricultural knowledge, has expanded significantly. However, as the number of nodes and relationships within these graphs increase, so too does their complexity. This complexity gives rise to new challenges in training and representing such large-scale knowledge graphs. It is therefore of great significance to investigate methods for speeding up the embedding process of agricultural knowledge graphs, while preserving their structural integrity and minimizing resource consumption. This research embarks on a novel exploration to address this issue. It stands out from previous studies by concentrating on a hierarchical representation model for agricultural knowledge graphs. The potential impacts of this research on propelling the advancement of the field and on addressing significant real-world problems are substantial. [Method/Process] To confront this challenge, we propose a hierarchical representation model for agricultural knowledge graphs rooted in the HARP framework. Our model leverages the inherent hierarchical features of the agricultural knowledge graph. It incorporates an improved random walk strategy based on relational paths to semantically model relationship objects within the agricultural knowledge graph. This innovative approach effectively retains the hierarchy and asymmetrical relationship structure of the nodes in the graph, setting our work apart from previous research. The validity of our proposed model is fortified by a strong foundation of theoretical and empirical evidence. [Results/Conclusions] Our experimental results reveal several key findings. First, the hierarchical random walk with path (HRWP) model using the LEIDEN algorithm can preserve the spatial structure more effectively and converge more quickly to the maximum modularity, in comparison to the HARP framework. Second, the fusion model employing HRWP takes less training time than the total training time of both models combined, without significantly affecting the time complexity of the original algorithm. Third, we observed that when traditional algorithms are integrated with HRWP, there is an average improvement of 2% across various indicators, with a substantial enhancement in non-neural network models. Therefore, our proposed model not only accurately represents the agricultural knowledge graph but also effectively reduces the training time. Despite the promising outcomes of our study, there remain areas of potential improvement. One such area is the need for a more detailed discussion on the hierarchical nature of relationship objects in future research. This provides potential avenues for future exploration in this field. The findings of this research carry profound implications for the development of agricultural knowledge management systems, offering an effective approach to handle the burgeoning complexity of knowledge graphs.

Key words: knowledge graph, walk, representation learning, the hierarchical random walk with path (HRWP) framework