# Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
## 1 Introduction  
Accurate detection of cow estrus and timely breeding are crucial for improving the reproductive rate of cows and herd productivity [1]. Cow estrus is characterized by multi-dimensional changes, including behavioral, appearance, and physiological aspects. Behaviorally, cows may exhibit increased activity, restlessness, and mounting behavior [2]. Appearance changes can involve mucous membrane congestion and vulva swelling [3]. Physiologically, there might be alterations in reproductive tract status and hormone levels [4]. These examples are not exhaustive; other signs may also be present. Traditional methods, such as using pedometers and observing mounting behavior, have limitations in accuracy and efficiency [5]. Moreover, estrus in cows is influenced by various factors, such as milk production, which adds to the complexity of estrus detection [6]. To address these challenges, automated systems using sensors (e.g., accelerometers, pedometers, and thermometers [7]) have been increasingly used to gather and interpret animal data [8]. These systems can provide more accurate and comprehensive information on cow estrus. In the booming era of smart agriculture, multi-source data fusion and intelligent analysis technologies offer innovative solutions for enhancing agricultural practices. Knowledge graphs have emerged as a powerful tool for organizing and managing multidimensional data related to cow estrus, encompassing behavioral, physiological, and environmental information [9]. Meanwhile, machine learning algorithms, particularly unsupervised learning methods like Isolation Forest (Iforest) and One-Class Support Vector Machine (OC-SVM), demonstrate significant advantages in handling complex data patterns and detecting anomalies [10]. This study integrates multidimensional estrus features through a knowledge graph [11] and uncovers hidden relationships among entities using the FP-Growth association rule mining method, thereby enriching the knowledge graph and boosting its predictive power [12]. Building on this foundation, the study further validates the effectiveness of association rules in normal and abnormal estrus detection using Boolean functions and the Iforest-OC-SVM model. The results highlight the practical value of association rules in enhancing decision-making for estrus management. In this study, we gathered knowledge from the domestic and international literature on cow estrus using web crawler technology. The entities required for the knowledge graph of cow estrus, namely, behavioral changes, appearance changes, and physiological changes, were filtered and refined. Then, the relationships between entities were established based on estrus data to initially construct the knowledge graph. To further improve the accuracy of estrus identification, we used the FP-Growth algorithm to mine strong association rules from the knowledge graph [13]. The FP-Growth algorithm is more efficient and scalable than the Apriori algorithm, as it avoids the need for generating candidate itemsets and reduces the number of database scans [14]. The strong association rules mined by the FP-Growth algorithm were then used to supplement the knowledge graph, enhancing its ability to accurately identify estrus. Moreover, by employing the FP-Growth association rule mining method, we revealed implicit relationships among entities, thereby enriching the knowledge graph and enhancing its predictive capabilities. Specifically, we optimized the algorithm through the development of an Incremental FP-Growth Algorithm for Dynamic Knowledge Expansion, which significantly improved its scalability and adaptability for future data expansions [9]. To validate the effectiveness of the strong association rules mined by the FP-Growth algorithm, two sets of experiments were conducted: one based on Boolean functions derived from association rule learning for normal estrus verification, and another using an Iforest-OC-SVM model with association rule-based feature weighting for abnormal estrus detection [15].