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Author: search.filters.author.Kone, V.Subject: search.filters.subject.Knowledge management

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    Enhancing Knowledge Management in the Construction Industry: Exploring the Impact of Semantic Web Technologies
    (Springer Science and Business Media Deutschland GmbH, 2025) Kone, V.; Mahesh, G.; Ingle, P.V.
    This research paper investigates the practical applications of Semantic Web technologies within the construction industry, specifically focusing on their role in knowledge management. The methodology employed for this research entails a systematic literature review, wherein relevant studies pertaining to Semantic Web technologies in the construction industry are gathered and meticulously analyzed. The study’s findings provide valuable insights into the benefits, challenges, and opportunities associated with the implementation of Semantic Web technologies for knowledge management purposes. The research reveals that Semantic Web technologies play a vital role in facilitating enhanced knowledge discovery, integration, and retrieval within construction projects. By establishing interoperability and integrating diverse data sources, these technologies effectively break down data silos and enable a comprehensive view of project information. Moreover, the study demonstrates that Semantic Web technologies support efficient collaboration, improve decision-making processes, and enable advanced analytics and predictive capabilities within construction projects. The significance of this research paper lies in its contribution to the understanding of Semantic Web technologies and their potential to revolutionize knowledge management practices within the construction industry. In conclusion, this research paper highlights the transformative impact of Semantic Web technologies on knowledge management in construction, establishing a robust foundation for future research and practical implementation in this dynamic industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    AN ONTOLOGY-DRIVEN BI-DIRECTIONAL WORKFLOW FOR INTEGRATING PROJECT MANAGEMENT DATA INTO THE IFC STANDARD
    (International Council for Research and Innovation in Building and Construction, 2025) Kone, V.; Mahesh, G.
    The evolution of Building Information Modelling (BIM) towards a data-centric paradigm is often hindered by challenges in semantic interoperability, particularly when integrating project management data with the Industry Foundation Classes (IFC) standard. While IFC enables syntactic data exchange, a persistent gap exists dynamically linking building geometry with the complex, relational information of project schedules, resources, and costs in a semantically consistent, interoperable manner. This paper presents a novel, bi-directional methodology that leverages Semantic Web technologies (RDF, OWL, SPARQL) to address this challenge. The core of the methodology is an ontology-driven workflow that uses two purpose-built ontologies: BIMOnto, a lightweight representation of the building asset derived from if cOWL, and IproK (Integrated Project Knowledge Ontology), which formally structures project management information across schedule, resource, and cost domains. The workflow enables both directions: (1) transforming IFC models into queryable knowledge graphs, and (2) programmatically generating new, enriched IFC models from unified knowledge graphs. This reverse transformation creates native, standards-compliant IFC entities for tasks (IfcTask), resources (IfcResource), costs (IfcCostItem), and their standard relationships (IfcRelAssignsToProduct, etc.), moving beyond custom property sets. The feasibility and effectiveness of this approach are validated through a case study using a multi-story residential building model, demonstrating the successful generation of a verifiable, integrated BIM artifact. The findings show that this ontology-driven framework significantly enhances data integration, creating truly interoperable models where process data becomes a first-class citizen within the BIM environment, advancing the potential for more intelligent, data-centric BIM practices throughout the project lifecycle. © © 2025 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.