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With the utility infrastructure domain becoming more technologically advanced with the modeling of all types of information across varying sectors, it is imperative to develop a domain ontology to enable the interoperability across the heterogeneous landscape of information modeling. This paper develops an ontology for the utility infrastructure domain by coupling the semantics of City Geography Markup Language (CityGML) Utility Network application domain extension (ADE)—a candidate open standard for modeling utilities—and domain glossaries, lists of utility terms with their textual definitions. First, a base ontology is formalized by abstracting the modeling information in the ADE through a series of semantic mappings. Second, a novel integrated natural language processing (NLP) approach is devised to automatically learn the semantics from the glossaries. The learning process includes the extraction of utility product terms using conditional random field (CRF) and the classification of semantic relationships between the terms using long short-term memory (LSTM) networks. Finally, the semantics learned from the glossaries are incorporated into the base ontology to result in a domain ontology for utility infrastructure. The NLP approach was evaluated using human-annotated test sets, and results show an average accuracy of 96% in term extraction and 86% in semantic relationship classification. For the case demonstration, a glossary of water product terms was learned to enrich the base ontology and the resulting ontology was evaluated to be an accurate, sufficient, and shared conceptualization of the domain. The newly developed ontology is expected to function effectively as an interoperability facilitator for the utility infrastructure domain, attributed to the semantic compatibility with existing utility modeling initiatives and the enriched/expandable semantic vocabulary. [ABSTRACT FROM AUTHOR]

Each year, thousands of new utilities are installed within the highway right-of-way (ROW) nationwide. This proliferation of utilities yields a great challenge to the traditional practice at road agencies to review, process, and track utility permit requests, document and manage utility conflicts, and check the regulatory compliance of newly installed utilities. This paper presents a new utility permitting system for road agencies, which is based on an object-oriented utility data model in Unified Modeling Language (UML) and its associated constraints in Object Constraint Language (OCL). The UML model defines the required information during the utility permitting process. The OCL constraints capture the regulatory requirements (mostly spatial constraints) that govern the installation of utilities within the ROW. A natural language processing (NLP) algorithm is designed to translate the requirements in textual documents into OCL expressions. The NLP algorithm achieves an average precision of 90% and a recall of 78%. The UML/OCL model is implemented in a spatial database to demonstrate its practical use in supporting automated utility permitting within the ROW. The novelty of this study includes (1) the capacity to model and store utility conflicts, including utility regulatory noncompliance, and (2) the incorporation of utility regulatory requirements as structured data for the automation of utility compliance checking. The newly developed system expedites the permitting process and contributes to the effective management of utilities within the ROW. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]