NLP-Based Query-Answering System for Information Extraction from Building Information Models
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 3
Abstract
The construction industry is information-intensive, and building information modeling (BIM) has been proposed as an information source for supporting decision making by construction project team members in the architecture, engineering, construction, and operation (AECO) industry. Because building information models contain more building data, further use of the aggregated building information to support construction and operation activities has become important. In Industry 4.0, similar-to-real-life virtual assistants, e.g., Apple’s Siri and Google Assistant, are becoming ever more popular. This research developed a query-answering (QA) system for BIM information extraction (IE) by using natural language processing (NLP) methods to build a virtual assistant for construction project team members. The architecture of the developed QA system for BIM IE consists of three major modules: natural language understanding, IE, and natural language generation. A Python-based prototype application was developed based on the architecture of the QA system for BIM IE to evaluate functionalities of the developed QA system using several BIM/industry foundation classes (IFC) models. Seven building information models and 127 test queries were utilized to evaluate the accuracy of the developed QA system for BIM IE. The experimental results indicated that the developed QA system for BIM IE achieved an 81.9 accuracy score. The developed NLP-based QA system for BIM is valid to provide relatively accurate answers based on natural language queries. The contributions of this research facilitate the development of virtual assistants in the AECO industry, and the architecture of the developed QA system can be extended to queries in other areas.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Bird, S., E. Loper, and E. Klein. 2009. Natural language processing with python. Newton, MA: O’Reilly Media.
buildingSMART. 2017. “Industry foundation classes 4.0.2.1 version 4.0—Addendum 2—Technical corrigendum 1.” Accessed May 21, 2020. https://standards.buildingsmart.org/IFC/RELEASE/IFC4/ADD2_TC1/HTML/link/alphabeticalorder-entities.htm.
buildingSMART. 2019. “Industry foundation classes (IFC).” Accessed June 10, 2019. https://technical.buildingsmart.org/standards/ifc.
buildingSMART. 2020. “IFC specifications database.” Accessed October 10, 2020. https://standards.buildingsmart.org/IFC/RELEASE/IFC4/ADD2_TC1/EXPRESS/IFC4.exp.
Celce-Murcia, M., D. Larsen-Freeman, and H. A. Williams. 1999. The grammar book: An ESL/EFL teacher’s course. Boston: Heinle & Heinle.
Cherpas, C. 1992. “Natural language processing, pragmatics, and verbal behavior.” Anal. Verbal Behav. 10 (1): 135. https://doi.org/10.1007/BF03392880.
Ghang, L., W. Jongsung, H. Sungil, and S. Yuna. 2011. “Metrics for quantifying the similarities and differences between IFC files.” J. Comput. Civ. Eng. 25 (2): 172–181. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000077.
ISO. 2004. Industrial automation systems and integration—Product data representation and exchange—Part 11: Description methods: The EXPRESS language reference manual. ISO 10303-11:2004. Geneva: ISO.
ISO. 2018. Industry foundation classes (IFC) for data sharing in the construction and facility management industries—Part 1: Data schema. ISO 16739-1:2018. Geneva: ISO.
Jongsung, W., L. Ghang, and C. Chiyon. 2013. “No-schema algorithm for extracting a partial model from an IFC instance model.” J. Comput. Civ. Eng. 27 (6): 585–592. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000320.
Karan, E. P., J. Irizarry, and J. Haymaker. 2016. “BIM and GIS integration and interoperability based on semantic web technology.” J. Comput. Civ. Eng. 30 (3): 04015043. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000519.
Kepuska, V., and G. Bohouta. 2018. “Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home).” In Proc., IEEE 8th Annual Computing and Communication Workshop and Conf. CCWC 2018, 99–103. New York: IEEE.
Kobayashi, Y., T. Yoshida, K. Iwata, H. Fujimura, and M. Akamine. 2019. “Out-of-domain slot value detection for spoken dialogue systems with context information.” In Proc., IEEE Spoken Language Technology Workshop, SLT 2018, 854–861. New York: IEEE.
Krijnen, T., and J. Beetz. 2018. “A SPARQL query engine for binary-formatted IFC building models.” Autom. Constr. 95 (Nov): 46–63. https://doi.org/10.1016/j.autcon.2018.07.014.
Li, H., A. Y. C. Wang, Y. Liu, D. Tang, Z. Lei, and W. Li. 2019. “An augmented transformer architecture for natural language generation tasks.” In Proc., Int. Conf. on Data Mining Workshops (ICDMW), 1131–1137. New York: IEEE. https://doi.org/10.1109/ICDMW48858.2019.9024754.
Lin, J., Z. Hu, J. Zhang, and F. Yu. 2016. “A natural-language-based approach to intelligent data retrieval and representation for cloud BIM.” Comput.-Aided Civ. Infrastruct. Eng. 31 (1): 18–33. https://doi.org/10.1111/mice.12151.
Liu, H., M. Lu, and M. Al-Hussein. 2016. “Ontology-based semantic approach for construction-oriented quantity take-off from BIM models in the light-frame building industry.” Adv. Eng. Inf. 30 (2): 190–207. https://doi.org/10.1016/j.aei.2016.03.001.
Manning, C. D., P. Raghavan, and H. Schütze. 2008. Introduction to information retrieval. Cambridge, UK: Cambridge University Press.
Montenegro, J. L. Z., C. A. Da Costa, R. D. R. Righi, A. Roehrs, and E. R. Farias. 2018. “A proposal for postpartum support based on natural language generation model.” In Proc., Int. Conf. on Computational Science and Computational Intelligence (CSCI), 756–759. New York: IEEE. https://doi.org/10.1109/CSCI46756.2018.00151.
Mujtaba, D., and N. Mahapatra. 2019. “Recent trends in natural language understanding for procedural knowledge.” In Proc., 6th Annual Conf. on Computational Science and Computational Intelligence, CSCI 2019, 420–424. New York: IEEE.
Nepal, M. P., S.-F. Sheryl, P. Rachel, and Z. Jiemin. 2013. “Ontology-based feature modeling for construction information extraction from a building information model.” J. Comput. Civ. Eng. 27 (5): 555–569. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000230.
Niknam, M., and S. Karshenas. 2015. “Integrating distributed sources of information for construction cost estimating using Semantic Web and Semantic Web Service technologies.” Autom. Constr. 57 (Sep): 222–238. https://doi.org/10.1016/j.autcon.2015.04.003.
Paredes-Valverde, M. A., R. Valencia-García, M. Á. Rodríguez-García, R. Colomo-Palacios, and G. Alor-Hernández. 2016. “A semantic-based approach for querying linked data using natural language.” J. Inf. Sci. 42 (6): 851–862. https://doi.org/10.1177/0165551515616311.
Park, Y., and S. Kang. 2019. “Natural language generation using dependency tree decoding for spoken dialog systems.” IEEE Access 7 (Dec): 7250–7258. https://doi.org/10.1109/ACCESS.2018.2889556.
Penn Treebank. 2003. “Penn Treebank P.O.S. tags.” Accessed June 7, 2020. https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html.
Rajpurkar, P., R. Jia, and P. Liang. 2018. “Know what you don’t know: Unanswerable questions for SQuAD.” In Proc., 56th Annual Meeting of the Association for Computational Linguistics, 784–789. Stroudsburg, PA: Association for Computational Linguistics.
Sacks, R., C. M. Eastman, P. M. Teicholz, and G. Lee. 2018. BIM handbook: A guide to building information modeling for owners, designers, engineers, contractors, and facility managers. Hoboken, NJ: Wiley.
Shi, X., Y. S. Liu, G. Gao, M. Gu, and H. Li. 2018. “IFCdiff: A content-based automatic comparison approach for IFC files.” Autom. Constr. 86 (Jun): 53–68. https://doi.org/10.1016/j.autcon.2017.10.013.
Spiegel, M., S. Lipschutz, and D. Spellman. 2009. Vector analysis (Schaum’s outlines). 2nd ed. New York: McGraw Hill.
Studer, R., A. Abecker, and S. Grimm. 2007. Semantic web services: Concepts, technologies, and applications. Edited by R. Studer, A. Abecker, and S. Grimm. Berlin: Springer.
Su, S. Y., and Y. N. Chen. 2019. “Investigating linguistic pattern ordering in hierarchical natural language generation.” In Proc., IEEE Spoken Language Technology Workshop, SLT 2018, 779–786. New York: IEEE.
Wang, N., and R. R. A. Issa. 2019. “Ontology-based building information model design change visualization.” In Proc., Workshop on Linked Building Data and Semantic Web Technologies (WLS2019), 53–61. Gainesville, FL: Smart Construction Informatics (SCI) Laboratory.
Wang, N., and R. R. A. Issa. 2020. “Natural language generation from building information models for intelligent NLP-based information extraction.” In Proc., EG-ICE 2020 Workshop on Intelligent Computing in Engineering, edited by L. C. Ungureanu and T. Hartmann, 275–284. Berlin: Universitätsverlag der TU Berlin.
WordNet. 2020. “WordNet: A lexical database for English.” Accessed June 7, 2020. https://wordnet.princeton.edu/.
Wu, S., Q. Shen, Y. Deng, and J. Cheng. 2019. “Natural-language-based intelligent retrieval engine for BIM object database.” Comput. Ind. 108 (Jun): 73–88. https://doi.org/10.1016/j.compind.2019.02.016.
Zhang, J., and N. M. El-Gohary. 2015. “Automated extraction of information from building information models into a semantic logic-based representation.” In Computing in civil engineering 2015, 173–180. Reston, VA: ASCE. https://doi.org/10.1061/9780784479247.022.
Zhang, J., and N. M. El-Gohary. 2016. “Extending building information models semiautomatically using semantic natural language processing techniques.” J. Comput. Civ. Eng. 30 (5): 331–346. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000536.
Zhang, L., and R. R. A. Issa. 2013. “Ontology-based partial building information model extraction.” J. Comput. Civ. Eng. 27 (6): 576–584. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000277.
Zheng, Y., G. Chen, and M. Huang. 2020. “Out-of-domain detection for natural language understanding in dialog systems.” IEEE/ACM Trans. Audio Speech Lang. Process. 28 (Apr): 1198–1209. https://doi.org/10.1109/TASLP.2020.2983593.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 3 • May 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 23, 2021
Accepted: Dec 21, 2021
Published online: Feb 21, 2022
Published in print: May 1, 2022
Discussion open until: Jul 21, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jose Guevara, Diego Rojas, Rana Khallaf, Gabriel Castelblanco, Navigating PPP Renegotiations in the Wake of COVID-19: Insights from a Toll Road Program, Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 10.1061/JLADAH.LADR-1082, 16, 1, (2024).
- Daniel Antonio Linares-Garcia, Nazila Roofigari-Esfahan, Practical Deployment of BIM-Interoperable Voice-Based Intelligent Virtual Agent Support Construction Worker Productivity, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-14475, 150, 11, (2024).
- Kasimir Forth, Jimmy Abualdenien, André Borrmann, Calculation of embodied GHG emissions in early building design stages using BIM and NLP-based semantic model healing, Energy and Buildings, 10.1016/j.enbuild.2023.112837, 284, (112837), (2023).
- Abdullahi B. Saka, Lukumon O. Oyedele, Lukman A. Akanbi, Sikiru A. Ganiyu, Daniel W.M. Chan, Sururah A. Bello, Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities, Advanced Engineering Informatics, 10.1016/j.aei.2022.101869, 55, (101869), (2023).
- Hazwani Shafei, Afiqah R. Radzi, Mohammed Algahtany, Rahimi A. Rahman, Construction 4.0 Technologies and Decision-Making: A Systematic Review and Gap Analysis, Buildings, 10.3390/buildings12122206, 12, 12, (2206), (2022).
- Yilun Zhou, Jianjun She, Yixuan Huang, Lingzhi Li, Lei Zhang, Jiashu Zhang, A Design for Safety (DFS) Semantic Framework Development Based on Natural Language Processing (NLP) for Automated Compliance Checking Using BIM: The Case of China, Buildings, 10.3390/buildings12060780, 12, 6, (780), (2022).
- Ning Wang, Raja R.A. Issa, Chimay J. Anumba, Transfer learning-based query classification for intelligent building information spoken dialogue, Automation in Construction, 10.1016/j.autcon.2022.104403, 141, (104403), (2022).