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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
Author
Cheng, Zhiwei 1 ; Ding Luyu 2   VIAFID ORCID Logo  ; Cheng, Peng 2   VIAFID ORCID Logo  ; Yu Helong 3   VIAFID ORCID Logo  ; Yang, Baozhu 4 ; Yu Ligen 2   VIAFID ORCID Logo  ; Li, Qifeng 2 

 College of Information Technology, Jilin Agricultural University, Changchun 130118, China, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; [email protected] (L.D.); [email protected] (C.P.); 
 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; [email protected] (L.D.); [email protected] (C.P.);, National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China 
 College of Information Technology, Jilin Agricultural University, Changchun 130118, China 
 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; [email protected] (L.D.); [email protected] (C.P.); 
First page
5235
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3211858555
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.