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Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2021-03-31 , DOI: 10.1016/j.aei.2021.101288
Xin Xu , Hubo Cai

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.



中文翻译:

基于本体和基于规则的自然语言处理方法来解释地下公用事业基础设施的文字规定

美国庞大的地下公用事业基础设施必须遵守众多法规。地下公用事业的合规检查需要对法规进行客观和一致的解释。但是,公用事业法规包含各种特定领域的术语以及有关地下公用设施的位置和净空的众多空间限制。对于口译员而言,要理解公用事业法规中的领域语义和空间语义都具有挑战性。为了应对这一挑战,本文采用了一种基于本体和基于规则的自然语言处理(NLP)框架,以自动执行公用事业法规的解释-法规信息的提取以及随后转化为逻辑条款的过程。已经开发了两种新的本体。城市产品本体(UPO)是特定于领域的,用于对领域概念建模并在公用事业法规中的异构术语之上捕获领域语义。空间本体(SO)由两层语义组成-语言空间表达和形式空间关系-为了更好地理解公用事业法规中的空间语言。对语法特征(使用常见的NLP技术捕获)和语义特征(使用本体捕获)定义的模式匹配规则进行编码,以进行信息提取。然后,通过本体将提取的信息元素映射到它们的语义对应关系,并最终将其转换成宗法逻辑(DL)子句,以实现语义和逻辑形式化。该方法已在公用事业住宿政策中针对与空间配置相关的要求进行了测试。结果表明,它在信息提取中达到98.2%的精度和94.7%的召回率,在语义形式化中达到94.4%的精度和90.1%召回率,在逻辑形式化方面达到83%的精度。

更新日期:2021-04-01
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