TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunications

2026-01-02

arXiv.org

AI Summary

This work addresses the inefficiency and heavy reliance on expert experience in troubleshooting telecommunications trouble tickets, which traditionally involve cumbersome and time-consuming procedures. To overcome these limitations, we propose the first end-to-end intelligent fault diagnosis system that integrates a domain-specific classification model, context-aware semantic retrieval, and a generative large language model. This synergistic architecture enables intelligent ticket routing, retrieval of similar historical cases, and automatic generation of diagnostic reports. By uniquely combining domain knowledge with context-aware reasoning capabilities, our approach significantly enhances both the accuracy and efficiency of fault resolution. Experimental results on real-world telecom datasets demonstrate that the proposed system outperforms state-of-the-art methods across key performance metrics.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Ticket troubleshooting refers to the process of analyzing and resolving problems that are reported through a ticketing system. In large organizations offering a wide range of services, this task is highly complex due to the diversity of submitted tickets and the need for specialized domain knowledge. In particular, troubleshooting in telecommunications (telecom) is a very time-consuming task as it requires experts to interpret ticket content, consult documentation, and search historical records to identify appropriate resolutions. This human-intensive approach not only delays issue resolution but also hinders overall operational efficiency. To enhance the effectiveness and efficiency of ticket troubleshooting in telecom, we propose TeleDoCTR, a novel telecom-related, domain-specific, and contextual troubleshooting system tailored for end-to-end ticket resolution in telecom. TeleDoCTR integrates both domain-specific ranking and generative models to automate key steps of the troubleshooting workflow which are: routing tickets to the appropriate expert team responsible for resolving the ticket (classification task), retrieving contextually and semantically similar historical tickets (retrieval task), and generating a detailed fault analysis report outlining the issue, root cause, and potential solutions (generation task). We evaluate TeleDoCTR on a real-world dataset from a telecom infrastructure and demonstrate that it achieves superior performance over existing state-of-the-art methods, significantly enhancing the accuracy and efficiency of the troubleshooting process.

Problem

• telecommunications
• ticket troubleshooting
• domain-specific
• operational efficiency
• fault resolution

Innovation

• domain-specific troubleshooting
• contextual retrieval
• generative fault analysis
• ticket routing
• telecom AI