Research and Implementation of Agronomic Entity and Attribute Extraction Based on Target Localization

文献类型： 外文期刊

第一作者： Guo, Xiuming

作者： Guo, Xiuming;Zhu, Yeping;Li, Shijuan;Liu, Shengping;Wu, Sheng

作者机构：

关键词： agronomy; text annotation; target location; named entity; attributes; knowledge graph; natural language processing (NLP); smart agriculture

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 2 期

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收录情况： SCI

摘要： The agronomic knowledge graph can provide accurate and reliable service support for agricultural production management. Agronomic knowledge often comes from unstructured text data, and efficient annotation of agricultural text data and construction of knowledge extraction models suitable for the characteristics of agronomic knowledge are two key points to create an agronomic knowledge graph. The proportion of attributes in agronomic knowledge is relatively high, but currently, the attribute annotation function of existing annotation tools is incomplete, and the annotation function and process are unclear. A scalable natural language annotation framework was proposed, which was able to flexibly configure the annotation process and annotation objects as needed, and the named entity was annotated in the corresponding mode. The current knowledge extraction models are mostly based on input text sequences, which has the problem of low feature utilization. However, the entities and attributes in agronomic knowledge have high similarity, and the position and type of entities and attributes can be directly calculated through their common features. An entity and attribute recognition model based on target localization, EntityDetectModel, was proposed. Firstly, Bert was used to extract text features with contextual information. Then, convolutional neural networks were used to extract features at different depths, and inter layer feature fusion was used to improve feature expression ability. Finally, the corresponding positions and types of named entities with different sizes were calculated based on the features at different depths. EntityDetectModel was compared with the other entity and relationship extraction models published in recent years and the results showed that the precision, recall, and F1 of EntityDetectModel were 91.0%, 83.4%, and 87.0%, respectively, which were superior to other comparison models. Using EntityDetectModel, a wheat agronomic knowledge graph was constructed.

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