Abstract:

[Objective] The present study was conducted to propose a method for organizing the knowledge of animal husbandry laws and regulations to achieve a more orderly organization of relevant knowledge. [Method] The online legal resources in the websites of flk.npc.gov.cn, pkulaw.com, lawyee.org, wkinfo.com.cn, and law.cnki.net were used as the data sources of animal husbandry laws and regulations, the relevant laws and regulations of animal husbandry were screened and obtained. Subsequently, the corresponding formulation authority, timeliness, effectiveness hierarchy, regulation category, issuing date, implementation date and year information were extracted, and the obtained data was preprocessed. The ontology was built, and the entities and relationships were defined. Using rule-based method, the entities and relationships from the obtained laws and regulations texts were extracted, and then temporarily stored in MySQL database. The SQLAlchemy database and py2neo database in Python were used to operate MySQL database and Neo4j Graph database to convert the data in MySQL into entity relationship triad, and then stored in Neo4j Graph database to complete the storage of knowledge graph of animal husbandry laws and regulations. [Result] A total of 287 laws and regulations related to animal husbandry were collected and classified based on whether the laws and regulations contained chapters. Among these, 211 laws and regulations contained chapters, totaling 1 470 chapters and 10 457 articles, whereas 76 laws and regulations did not contain chapters, totaling 2 145 articles. There were 10 types of entities defined, including ′laws and regulations′, ′issuing authority′, ′timeliness′, ′effectiveness hierarchy′, ′regulation category′, ′chapter′, ′article′, etc. There were 10 types of relationships defined, including ′the relationship of animal husbandry laws and regulations to category of laws and regulations ′, ′the relationship of animal husbandry laws and regulations to timeliness of laws and regulations′, ′the relationship of animal husbandry laws and regulations to issuing authority of laws and regulations′, and ′the relationship of animal husbandry laws and regulations to effectiveness hierarchy of laws and regulations′, etc. A knowledge graph of animal husbandry laws and regulations covering 14 936 entities and 16 339 relationships was constructed. [Conclusion] This method can be applied to organize the knowledge on animal husbandry laws and regulations, making the organization of knowledge more systematic and closely related.

Key words: animal husbandry, knowledge graph, law and regulation, Neo4j