Joint Extraction of Multiple Relations and Entities from Building Code Clauses

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to addr...

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Bibliographic Details
Published inApplied sciences Vol. 10; no. 20; p. 7103
Main Authors Li, Fulin, Song, Yuanbin, Shan, Yongwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2020
Subjects
Algorithms
Analysis
automated code compliance checking
Automation
building code
Building codes
Building law
Computational linguistics
Data mining
Deep learning
Engineering
Language
Language processing
Machine learning
Methods
Natural language
Natural language interfaces
Ontology
Regulation
regulation information extraction
Regulatory compliance
Roles
Semantic relations
Semantics
China
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Summary:The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10207103