# Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
## 6 Conclusions and Future Work  
In this study, we constructed and completed a knowledge graph for cow estrus by systematically gathering and organizing 812 academic literature studies using web crawlers. The process involved rigorous de-duplication, screening, and in-depth entity identification to ensure the accuracy and comprehensiveness of the data. Through entity and relationship extraction, we integrated the knowledge into a structured framework encompassing 86 core entities and 9 key relationships. This structured approach not only organized the dispersed knowledge related to cow estrus but also provided a comprehensive foundation for further analysis and application. To enhance the graph, we employed association rule mining using the Apriori and FP-Growth algorithms. This process uncovered 17 significant rules that highlight key behavioral and physiological characteristics during estrus. These rules were meticulously integrated into the knowledge graph, expanding its relationships and significantly improving its accuracy and usefulness for estrus determination. The association rule mining step was crucial for uncovering hidden relationships within the data, thereby enriching the graph's content and providing deeper insights into estrus behaviors and physiological changes. Then, we proposed an incremental FP-Growth algorithm that dynamically updated the FP-Tree. The incremental FP-Growth algorithm enhances scalability and lays a solid foundation for future expansion to more complex and diverse datasets. This makes it better able to handle the increasing volume and variety of data that may arise in cow heat research, ensuring efficient and effective data processing and analysis. To validate this, we conducted experiments comparing the initial knowledge graph approach with the enhanced approach incorporating strong association rules. The enhanced method, which utilized association rules, outperformed the initial method across all evaluation metrics, including accuracy, precision, recall, and F1 score. This highlights the superior performance of the association rule-based approach in estrus identification. Figure 12: Performance Comparison of Anomaly Detection Models. Furthermore, we performed validation experiments based on quantified association rules to assess their effectiveness in detecting abnormal estrus using the Iforest-OCSVM model. These experiments confirmed that the association rules not only improved the accuracy of normal estrus detection but also enhanced the identification of abnormal estrus, providing a more reliable basis for decision-making in estrus management. This study thus provides a robust technical foundation for intelligent reasoning and early warning in cow estrus management, while advancing breeding efficiency and livestock management. In future work, we will explore the optimization of preprocessing steps for sensor data, particularly focusing on standardization and normalization. Sensor data, often collected from various devices with different units and value ranges, can significantly impact model performance. We plan to investigate data standardization techniques, such as Z-Score normalization, to unify the value ranges of diverse sensor data, thereby enhancing model stability and accuracy. Additionally, we aim to develop methods that dynamically adjust preprocessing parameters in real time to accommodate changes in sensor data. We will also study how to improve data consistency and model robustness when integrating data from multiple sensors through standardized processing. These efforts are expected to further improve the model's performance and reliability in handling complex sensor data, providing a more effective solution for estrus anomaly detection in cows. In addition, we will focus on further validating these rules and exploring additional applications of the knowledge graph in cow estrus identification, with the aim of optimizing breeding programs and driving innovation in the cow breeding industry. Conceptualization, Z.C. and L.D. ; methodology, Z.C. ; software, Z.C.