Semantic Data Integration Using Artificial Intelligence for Interoperable Construction Information Systems
Keywords:
Semantic Web, Data Interoperability, Artificial Intelligence, Construction Information Systems, Ontology, BIMAbstract
The construction industry has seen an increasing demand for interoperable data systems capable of integrating heterogeneous information sources. Semantic data integration, driven by Artificial Intelligence (AI), offers a powerful approach to enhancing data interoperability across diverse construction domains. This paper explores the role of AI in semantic integration of construction data, identifying key methodologies and challenges while proposing an AI-enabled semantic framework for enhanced interoperability in Building Information Modeling (BIM) and related systems.
References
Zhang, J., et al. (2020). Ontology-based integration of BIM and construction management. Automation in Construction, 110, 103036.
Pauwels, P., & Zhang, S. (2018). Semantic web technologies in AEC industry. Automation in Construction, 91, 1–17.
Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130–134. https://doi.org/10.52783/jes.9328
Gao, G., et al. (2019). NLP for extracting construction concepts. Advanced Engineering Informatics, 40, 140–154.
Farias, J., et al. (2022). Ontology-driven data interoperability. Journal of Information Technology in Construction, 27, 233–249.
Ding, L., et al. (2021). Semantic integration in infrastructure modeling. Advanced Engineering Informatics, 47, 101265.
Liu, R., & Issa, R. R. A. (2012). Ontology-based semantic mapping for interoperability of BIM and construction management applications. Automation in Construction, 20(2), 199–206.
Gummadi, V. P. K. (2020). API design and implementation: RAML and OpenAPI specification. Journal of Electrical Systems, 16(4). https://doi.org/10.52783/jes.9329
El-Diraby, T. E., Osarenkhoe, A., & Abdul-Malak, M. A. (2021). AI-enabled ontologies for construction knowledge management. Journal of Computing in Civil Engineering, 35(3), 04021004.
Beetz, J., van Leeuwen, J. P., & de Vries, B. (2009). IfcOWL: A case of transforming EXPRESS schemas into OWL. AI EDAM, 23(1), 89–101.
Kang, T. W., & Hong, C. H. (2015). A study on software architecture for BIM/GIS-based facility management system. Automation in Construction, 54, 25–38.
Perera, S., Nanayakkara, S., Rodrigo, M. N. N., Senaratne, S., & Weinand, R. (2020). Artificial intelligence in construction: A review of current applications and future directions. Automation in Construction, 119, 103312.
Niknam, M., Karshenas, S., & Kandil, A. (2021). A machine learning framework for mapping construction project documents to industry ontologies. Journal of Construction Engineering and Management, 147(4), 04021010.
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Copyright (c) 2022 Benítez Ruiz Mansoor (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.