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.

翻译：执法报告包含结构化字段与书面叙事内容。然而，许多用于案件审查、警务培训和调查的事件事实以自然语言形式存在，需要人工阅读。我们提出了一种基于符号方法的框架，用于将叙事文本转换为证据关联事实。本研究的目的是评估叙事文本在仅从非结构化文本中恢复事件细节方面的价值，并构建融合时间线索与领域公理的时间图谱。我们通过隐去个人标识符、语义解析、谓词到本体的映射及推理来实现该目标。我们在450份财产犯罪报告及简短人工复核中评估了该符号方法。系统提取的事件中，54.1%的置信度得分不低于0.80，93.7%通过PropBank--VerbNet--WordNet语义路径完成映射。事件启动、被盗物品和时间线索的判定达到100%一致性，而强制入侵解释的一致性较低。