Can agents improve schema matching?
Implementing Systemic Thinking for Automatic Schema Matching: An Agent-Based Modeling Approach
January 9, 2025
https://arxiv.org/pdf/2501.04136This paper explores using a multi-agent system (MAS) to automate schema matching, a process of identifying semantically related elements between different schemas (like databases). It proposes a system called Reflex-SMAS, where each schema element is an agent that interacts with other agents to find the best matches.
Key points for LLM-based multi-agent systems:
- Decentralized approach: Each agent operates independently, increasing system robustness and adaptability. This aligns with the distributed nature of some LLM architectures.
- Emergent behavior: The overall solution (schema mapping) emerges from the interactions of individual agents, mirroring how complex behaviors can arise from simpler LLM agents.
- Stochasticity: Randomness in similarity calculation and other processes allows exploring a wider solution space. This can be relevant to LLM agents exploring different response strategies.
- Self-organization: Agents cooperate to reach a consensus on best matches, demonstrating how LLM agents can collaborate to achieve a common goal.
- Adaptability: The system dynamically adjusts to different matching scenarios without manual intervention, highlighting the potential for self-learning and adapting LLM agents.
- Efficiency through reduced human intervention: Automating optimization and configuration aligns with the goal of autonomous LLM-based multi-agent systems.