Construction of a Multi-Source, Heterogeneous Rice Disease and Pest Knowledge Graph Based on the MARBC Model

Abstract

Diseases and pests have a significant impact on rice production, affecting both yield and quality. Therefore, their effective management and control are crucial for successful rice cultivation. However, current research based on rice diseases and pests (RDPs) encounters challenges such as data scarcity, the integration of multi-source heterogeneous data and usability issues related to knowledge graphs. To tackle these issues, this paper proposes a novel entity and relationship extraction model called Multi-head Attention RoBERTa BiLSTM CRF (MARBC). Specifically, the MARBC model utilizes RoBERTa to obtain related word vector representations, and then employs BiLSTM to extract features from within the input sequences. By integrating a multi-head attention mechanism, the model retrieves contextual information and relevance from the text, enhancing the accuracy and depth of the knowledge graph. Additionally, Conditional Random Fields are used to model sequence labeling for entities and relationships. Experimental results demonstrate the model’s impressive performance, achieving precision, recall, and F1 scores of 95.31%, 93.58%, and 94.44%, respectively. Furthermore, this paper constructs a dedicated knowledge graph for RDPs from both ontology and data layers. By effectively integrating and organizing multi-source heterogeneous RDP data, this paper provides valuable resources and decision support for agricultural researchers and farmers.