Building a knowledge graph from telecommunication data

The paper presents a method for building a knowledge graph from telecommunication data using proprietary and reference models commonly employed in the telecommunications domain. Reference models are based on those included in the framework developed by the TM Forum consortium. The proposed approach involves building a knowledge graph for proprietary models through automated processing of autotest log files and billing system database tables. The relevance of knowledge graphs stems from their structured and semantic nature, as well as their potential for applying machine learning algorithms to generate recommendations for optimizing telecommunication processes and systems. A method based on a multi-step reasoning approach is proposed for creating interpretable recommendations by predicting and restoring missing links in a proprietary knowledge graph. This approach treats multi-step reasoning as a question-answering task using natural language processing techniques. The implementation of the proposed solution, leveraging a transformer-based neural network architecture, yielded interpretable results while maintaining metric values comparable to existing methods