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ONTOLOGY DEVELOPMENT USING LANGUAGE MODEL-BASED NAMED ENTITY RECOGNITION FOR INTEGRATED CONSTRUCTION INFORMATION.

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Title: ONTOLOGY DEVELOPMENT USING LANGUAGE MODEL-BASED NAMED ENTITY RECOGNITION FOR INTEGRATED CONSTRUCTION INFORMATION.
Authors: CHOI, Goeun1, KWON, Soonwook2 swkwon@skku.edu, SONG, Jinwoo3, AKBAR, Ali1, HONG, Jung-taek1
Source: Journal of Civil Engineering & Management. 2026, Vol. 32 Issue 4, p548-562. 15p.
Subjects: Building information modeling, Data integration, Ontologies (Information retrieval), Quantity surveyors, Information retrieval, Language models
Abstract: Named Entity Recognition (NER) is crucial for building knowledge bases and facilitating semantic search in the construction industry. While conventional NER models can identify general entities such as spatial and organizational information, extracting domain-specific entities, like materials and dimensions from construction-related texts -particularly in Bill of Quantities (BoQ) and Building Information Modeling (BIM) parameters - remains challenging extensive manual annotation. Key entity categories were defined, and datasets from four BoQ and two BIM sources were annotated to establish ground truth labels. A semi-automated labelling process was introduced to streamline annotation and improve training efficiency. Experimental results demonstrate that the proposed framework reduces annotation time by nearly threefold compared to manual processes. This study developed a BERT-based NER model achieving F1 scores ranging from 0.81 to 0.97, with higher performance for well-defined construction parameters (name, material, size, thickness, diameter, length, type: 0.95-0.97) compared to miscellaneous text entities (0.81). Despite extensive research in construction NLP, existing approaches fail to address the integration challenges between heterogeneous BIM-BoQ data formats and lack domain-specific entity recognition capabilities. The extracted entities are aligned with standardized formats using semantic text similarity techniques. This ontology-based integration enhances data consistency, interoperability, and retrieval accuracy, improving semantic alignment while minimizing discrepancies from heterogeneous terminology. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Civil Engineering & Management is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: ONTOLOGY DEVELOPMENT USING LANGUAGE MODEL-BASED NAMED ENTITY RECOGNITION FOR INTEGRATED CONSTRUCTION INFORMATION.
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  Data: <searchLink fieldCode="AR" term="%22CHOI%2C+Goeun%22">CHOI, Goeun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22KWON%2C+Soonwook%22">KWON, Soonwook</searchLink><relatesTo>2</relatesTo><i> swkwon@skku.edu</i><br /><searchLink fieldCode="AR" term="%22SONG%2C+Jinwoo%22">SONG, Jinwoo</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22AKBAR%2C+Ali%22">AKBAR, Ali</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22HONG%2C+Jung-taek%22">HONG, Jung-taek</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Building+information+modeling%22">Building information modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Ontologies+%28Information+retrieval%29%22">Ontologies (Information retrieval)</searchLink><br /><searchLink fieldCode="DE" term="%22Quantity+surveyors%22">Quantity surveyors</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink>
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  Label: Abstract
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  Data: Named Entity Recognition (NER) is crucial for building knowledge bases and facilitating semantic search in the construction industry. While conventional NER models can identify general entities such as spatial and organizational information, extracting domain-specific entities, like materials and dimensions from construction-related texts -particularly in Bill of Quantities (BoQ) and Building Information Modeling (BIM) parameters - remains challenging extensive manual annotation. Key entity categories were defined, and datasets from four BoQ and two BIM sources were annotated to establish ground truth labels. A semi-automated labelling process was introduced to streamline annotation and improve training efficiency. Experimental results demonstrate that the proposed framework reduces annotation time by nearly threefold compared to manual processes. This study developed a BERT-based NER model achieving F1 scores ranging from 0.81 to 0.97, with higher performance for well-defined construction parameters (name, material, size, thickness, diameter, length, type: 0.95-0.97) compared to miscellaneous text entities (0.81). Despite extensive research in construction NLP, existing approaches fail to address the integration challenges between heterogeneous BIM-BoQ data formats and lack domain-specific entity recognition capabilities. The extracted entities are aligned with standardized formats using semantic text similarity techniques. This ontology-based integration enhances data consistency, interoperability, and retrieval accuracy, improving semantic alignment while minimizing discrepancies from heterogeneous terminology. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Civil Engineering & Management is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3846/jcem.2026.26519
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      – SubjectFull: Language models
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      – TitleFull: ONTOLOGY DEVELOPMENT USING LANGUAGE MODEL-BASED NAMED ENTITY RECOGNITION FOR INTEGRATED CONSTRUCTION INFORMATION.
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            NameFull: CHOI, Goeun
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              Text: 2026
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