Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking

Abstract

Automated regulatory compliance checking requires automated extraction of requirements from regulatory textual documents and their formalization in a computer-processable rule representation. Such information extraction (IE) is a challenging task that requires complex analysis and processing of text. Natural language processing (NLP) aims to enable computers to process natural language text in a human-like manner. This paper proposes a semantic, rule-based NLP approach for automated IE from construction regulatory documents. The proposed approach uses a set of pattern-matching-based IE rules and conflict resolution (CR) rules in IE. A variety of syntactic (syntax/grammar-related) and semantic (meaning/context-related) text features are used in the patterns of the IE and CR rules. Phrase structure grammar (PSG)-based phrasal tags and separation and sequencing of semantic information elements are proposed and used to reduce the number of needed patterns. An ontology is used to aid in the recognition of semantic text features (concepts and relations). The proposed IE algorithms were tested in extracting quantitative requirements from the 2009 International Building Code and achieved 0.969 and 0.944 precision and recall, respectively.