Research and Construction of Knowledge Map of Golden Pomfret Based on LA-CANER Model

To address the issues of fragmented species information, low knowledge extraction efficiency, and insufficient utilization in the aquaculture domain, the main objective of this study is to construct the first knowledge graph for the Golden Pomfret aquaculture field and optimize the named entity recognition (NER) methods used in the construction process. The dataset contains challenges such as long text processing, strong local context dependencies, and entity sample imbalance, which result in low information extraction efficiency, recognition errors or omissions, and weak model generalization. This paper proposes a novel named entity recognition model, LA-CANER (Local Attention-Category Awareness NER), which combines local attention mechanisms with category awareness to improve both the accuracy and speed of NER. The constructed knowledge graph provides significant scientific knowledge support to Golden Pomfret aquaculture workers. First, by integrating and standardizing multi-source information, the knowledge graph offers comprehensive and accurate data, supporting decision-making for aquaculture management. The graph enables precise reasoning based on disease symptoms, environmental factors, and historical production data, helping workers identify potential risks early and take preventive actions. Furthermore, the knowledge graph can be integrated with large models like GPT-4 and DeepSeek-R1. By providing structured knowledge and rules, the graph enhances the reasoning and decision-making capabilities of these models. This promotes the application of smart aquaculture technologies and enables precision farming, ultimately increasing overall industry efficiency.