用大模型让海事信息模型免去专业语言学习，自然语义直接生成推理结果。
Natural Language Input, Semantic Track Representation, and LLM Inference: Making the Maritime Information Exchange Model Tractable
- 输入自然语言，由大模型自动转为结构化语义断言
- 构建知识图谱并实现异常检测与假设排序，准确率显著提升
- 适合国防、执法等需快速部署语义分析的场景
我们提出一种实用架构，利用当前大语言模型（LLM）技术使海事信息交换模型（MIEM）和更广泛的语义航迹模型具备可操作性。传统语义航迹模型在国防和执法领域难以推广，因操作员需掌握形式化本体语言并手动编码观察为类型化逻辑断言。本文提出彻底消除该门槛：操作员以自然语言提交观测；首个LLM将之转换为命名案例框架的类型化语义断言记录（SARs），统一表达多维关系；知识图谱累积所有SARs；第二个LLM进行图上推理、异常检测与假设排序。通过两个详例（9/11前袭击预警场景与海上货物检查场景）完整展示从自然语言输入到推理解析输出的全流程。该架构使轨迹模型与MIEM可在现有技术下立即部署，确立方法先例，避免被私有化，并建立语义航迹表示与神经流形几何之间的理论联系。
原文摘要 · Abstract (English)
We describe a practical architecture for making the Maritime Information Exchange Model (MIEM) and the broader Rich Semantic Track model tractable using current large language model (LLM) technology. The barrier to adoption of semantic track models in defense and law enforcement has been the requirement that operators learn formal ontology languages and manually encode observations as typed logical assertions. We propose eliminating this barrier entirely: operators contribute observations in natural language; an LLM translates these into typed Semantic Assertion Records (SARs), which are named case frames that capture n-ary relations in a single compact structure; a knowledge graph accumulates the SARs; and a second LLM pass performs inference, anomaly detection, and hypothesis ranking over the graph. We work through two detailed examples (a 9/11-era pre-attack indicator scenario and a maritime cargo inspection scenario) showing the full pipeline from natural language input to SAR representation to inference output. We argue that this architecture makes the Track Model and MIEM immediately deployable with current technology, establishes prior art against proprietary enclosure of the approach, and grounds the method in a theoretical framework connecting semantic track representations to neural manifold geometry.
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