Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics

2026-09-15

AI Summary

为解决生成式AI在处理多步骤操作问题时的不可靠性，提出通过虚拟知识图谱进行符号分离的方法，提高任务成功率并减少数据完整性错误。

Abstract

Generative AI promises natural language access to the massive numerical telemetry of data centers and Industry 4.0 installations, yet text-to-query and tool-using agents stay unreliable: even frontier models answer little more than half of real-world database questions, and far fewer of the multi-step, operational ones, because the LLM must compose how heterogeneous sources relate and hallucinates the relations, not just the fields. We propose symbolic separation: a deep agent reasons freely but may act on data only through an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation. Unlike a tool API's interface contract, this domain-semantic contract turns a complex question into one validated graph traversal instead of LLM-inferred joins. Instantiated as the Neurosymbolic Deep Analyst and evaluated on 49.9 TB of superconputer telemetry against a rigid workflow and a non-symbolic ablation, it raises end-to-end task success from 43% to 86%, prevents silent data-integrity errors that no syntactic check catches, and cuts token cost by 2.4x, letting a smaller on-premise model outperform a larger one.

Problem

• Generative AI
• Operational Data Analytics
• Knowledge Graphs
• Data Integrity
• Virtual Knowledge Graph

Innovation

• Symbolic Separation
• Virtual Knowledge Graph
• Ontology-constrained
• Deep Agent
• Data Integrity