Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
<b>Background:</b> 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. <b>Methods:</b...
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| Format: | Article |
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MDPI AG
2025-05-01
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| author | Zhiwei Cheng Luyu Ding Cheng Peng Helong Yu Baozhu Yang Ligen Yu Qifeng Li |
| author_facet | Zhiwei Cheng Luyu Ding Cheng Peng Helong Yu Baozhu Yang Ligen Yu Qifeng Li |
| author_sort | Zhiwei Cheng |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-15dce62d17e5402ab5d0a72f1775d93e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-15dce62d17e5402ab5d0a72f1775d93e2025-08-20T01:56:13ZengMDPI AGApplied Sciences2076-34172025-05-011510523510.3390/app15105235Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule MiningZhiwei Cheng0Luyu Ding1Cheng Peng2Helong Yu3Baozhu Yang4Ligen Yu5Qifeng Li6College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2076-3417/15/10/5235cow estrusknowledge graphknowledge complementationassociation rule algorithm |
| spellingShingle | Zhiwei Cheng Luyu Ding Cheng Peng Helong Yu Baozhu Yang Ligen Yu Qifeng Li Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining Applied Sciences cow estrus knowledge graph knowledge complementation association rule algorithm |
| title | Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining |
| title_full | Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining |
| title_fullStr | Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining |
| title_full_unstemmed | Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining |
| title_short | Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining |
| title_sort | construction and completion of the knowledge graph for cow estrus with the association rule mining |
| topic | cow estrus knowledge graph knowledge complementation association rule algorithm |
| url | https://www.mdpi.com/2076-3417/15/10/5235 |
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