Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining

文献类型： 外文期刊

第一作者： Cheng, Zhiwei

作者： Cheng, Zhiwei;Yu, Helong;Cheng, Zhiwei;Ding, Luyu;Peng, Cheng;Yang, Baozhu;Yu, Ligen;Li, Qifeng;Ding, Luyu;Peng, Cheng;Yu, Ligen;Li, Qifeng

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关键词： cow estrus; knowledge graph; knowledge complementation; association rule algorithm

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

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年卷期： 2025 年 15 卷 10 期

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收录情况： SCI

摘要： Background: Accurate estrus identification in dairy cows is essential for enhancing reproductive efficiency and economic performance. The dispersed nature of estrus data and individual cow differences pose significant challenges for accurate identification. Methods: This study gathered cow estrus data from 812 literature sources using Python 3.9 crawler technology. The data were then preprocessed using CiteSpace 6.4. We constructed a knowledge graph depicting physiological, behavioral, and appearance changes during estrus through entity and relationship extraction. To uncover potential relationships within the graph, we applied and compared two association rule algorithms: FP-Growth and Apriori. We utilized Boolean functions derived from association rule learning to validate the ability of the rules to identify normal estrus. Additionally, we employed an enhanced Iforest-OCSVM anomaly detection model to assess the performance of the association rules in detecting abnormal estrus. Furthermore, we optimized the Incremental FP-Growth Algorithm for Dynamic Knowledge Expansion. Results: Based on the initial knowledge graph with 86 entities and 9 relationships, mining added 17 new strong association relationships marked by 'with', enhancing its completeness and providing deeper insights into estrus behaviors and physiological changes. Furthermore, these strong association rules exhibited notable effectiveness in both normal and abnormal estrus detection, validating their robustness in practical applications. The algorithm's optimization bolstered its scalability, making it more adaptable to future data expansions and complex knowledge integrations. Conclusions: By constructing a knowledge graph that integrates physiological, behavioral, and appearance changes during estrus, we established a comprehensive framework for understanding cow estrus. Association rule mining, particularly with the FP-Growth algorithm, added 17 new strong association relationships to the graph, enriching its content and offering deeper insights into estrus behaviors and physiological changes. The strong association rules derived from FP-Growth demonstrated notable effectiveness in both normal and abnormal estrus detection, validating their robustness and practical utility in enhancing estrus identification accuracy, and providing a robust foundation for future multi-dimensional estrus research.

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