ISSN 1000-1239 CN 11-1777/TP

Special Issue: 2020数据驱动网络专题

### Building Network Domain Knowledge Graph from Heterogeneous YANG Models

Dong Yongqiang1,3, Wang Xin1, Liu Yongbo1, Yang Wang2,3

1. 1(School of Computer Science and Engineering, Southeast University, Nanjing 211189);2(School of Cyber Science and Engineering, Southeast University, Nanjing 211189);3(Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
• Online:2020-04-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61971131) and the National Key Research and Development Program of China (2018YFB1800205).

Abstract: With the continuous expansion of network scale, network management and operation face great challenges of complexity and heterogeneity. The existing intelligent network operation approaches lack a unified data model at the knowledge level to guide the process of network big data. As a data modeling language, YANG has been used to model the configuration and state data transmitted by NETCONF protocol. This paper proposes an intelligent network operation scheme which builds network domain knowledge graph from heterogeneous YANG models. As per YANG language specification, the scheme proposes the basic principles of network domain ontology construction, forming an ontology structure containing 51 classes and more than 70 properties. Then, the heterogeneous YANG models from different standardization organizations and vendors are extracted and instantiated into network domain knowledge graph. Entity alignment methods are therein employed to explore the semantic co-reference relationships among uni-source YANG models. The acquired knowledge graph provides a unified semantic framework to organize massive network operation data, which thus eliminates the requirement to construct AIOps ontology manually. As such, the configuration management and operational maintenance of networks could be greatly simplified, enlightening new solutions for network performance optimization and anomaly detection problems.

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