How can KG embeddings improve support ticket routing?
Efficient support ticket resolution using Knowledge Graphs
January 4, 2025
https://arxiv.org/pdf/2501.00461This paper proposes a system for efficiently resolving customer support tickets by recommending the best-suited engineers or engineering teams ("swarms"). It leverages knowledge graphs, graph neural networks (GNNs), and natural language understanding (NLU) including very large language models (VLLMs) to analyze ticket content, engineer expertise, past collaborations (swarms), and knowledge base articles.
Key points for LLM-based multi-agent systems:
- LLMs for Rich Contextualization: VLLMs are used to process text from tickets, communications, and KBAs, generating rich contextual embeddings for both agents (engineers) and tasks (tickets).
- Knowledge Graph for Agent Relationships: A knowledge graph represents relationships between engineers, tickets, and KBAs, enabling the system to learn from past collaborations and expertise.
- GNNs for Agent Ranking: GNNs operating on the knowledge graph produce dynamic agent embeddings that capture their expertise and collaboration history, facilitating a learning-to-rank approach.
- Multi-Agent Collaboration (Swarming): The system specifically addresses scenarios where multiple agents need to collaborate to solve complex tickets, leveraging swarm data for training.
- Dynamic Agent Selection: The proposed model allows for dynamic recommendations, taking into account the current state of the ticket and who is already working on it.