构建企业级知识图谱，实现自动推理与精准对齐。
Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale
- 三阶段统一框架：构造、对齐、推理一体化
- 在复杂图推理中达94%准确率，优于现有大模型
- 适合需要可靠逻辑决策的企业级系统开发
企业积累大量数据却多呈杂乱状态，难以支持全面决策。现有神经符号方法依赖分离流程，易产生错误传播。本文提出大型本体模型（LOM），将本体构建、语义对齐与逻辑推理整合为端到端架构。LOM采用构造-对齐-推理（CAR）流程：首先从原始数据自主构建领域专属本体体系；接着利用图感知编码器与强化学习，使神经生成结果与结构现实对齐；最后在构建的拓扑结构、节点属性与关系类型上执行确定性推理。我们在基于多样化真实企业数据集构建的综合基准上评估了LOM。实验表明，LOM-4B在本体补全任务中达到88.8%准确率，在复杂图推理任务中达94%，显著优于当前顶尖大模型。结果验证了自主逻辑构建对实现确定性企业级智能至关重要。
原文摘要 · Abstract (English)
While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive information. Existing neuro-symbolic approaches rely on disjoint pipelines and struggle with error propagation. We introduce the large ontology model (LOM), a unified framework that seamlessly integrates ontology construction, semantic alignment, and logical reasoning into a single end-to-end architecture. LOM employs a construct-align-reason (CAR) pipeline, leveraging its unified architecture across all three stages: it first autonomously constructs a domain-specific ontological universe from raw data, then aligns neural generation with this structural reality using a graph-aware encoder and reinforcement learning, and finally executes deterministic reasoning over the constructed topology, node attributes and relation types. We evaluate LOM on a comprehensive benchmark constructed from diverse real-world enterprise datasets. Experimental results demonstrate that LOM-4B achieves 88.8% accuracy in ontology completion and 94% in complex graph reasoning tasks, significantly outperforming state-of-the-art LLMs. These findings validate that autonomous logical construction is essential for achieving deterministic, enterprise-grade intelligence.
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