将警报报告中的自然语言转化为可推理的结构化事实，提升案件信息提取效率。
Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports
- 用符号方法解析文本，构建带时间线索和领域规则的事件图谱
- 54.1%事件提取置信度超0.8，93.7%通过语义路径映射成功
- 适合公安情报分析、案件复盘与训练系统开发人员使用
执法报告包含结构化字段和自然语言叙述。但许多案件细节存在于非结构化文本中，需人工阅读。本文提出一种基于符号的方法，将叙述转换为带证据关联的事实。目标是仅从非结构化文本中恢复案件细节，并构建含时间线索与领域公理的时序图谱。方法包括匿名化处理、语义解析、谓词到本体映射与推理。在450份财产犯罪报告上评估，系统提取事件中54.1%的置信度达0.8以上，93.7%可通过PropBank--VerbNet--WordNet语义路径映射。事件起始、被盗物品及时间线索达成100%人工一致，强制进入判定一致性较低。
原文摘要 · Abstract (English)
Law enforcement reports contain structured fields and written narratives. However, many incident facts that are needed for review, police training, and investigations are in natural language and require manual reading. We propose a framework using symbolic methods for converting narratives into evidence-linked facts. Our objective is to measure the value of narratives to recover incident details only from the unstructured text and build temporal graphs with time cues and domain axioms. We achieve this by redacting personal identifiers, semantic parsing, predicate mapping to ontology, and reasoning. We evaluate the symbolic approach on 450 property crime reports and a short human review. Of the extracted events from the system, 54.1% had a confidence score of at least 0.80 and 93.7% were mapped through the PropBank--VerbNet--WordNet semantic path. 100% agreement was reached on incident initiation, stolen items, and temporal cues and lower agreement for forced entry interpretation.
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