Search Results (6)

The aging population in Europe and other developed regions is accelerating the demand for adaptable domestic environments that support independent living and care at home. In this context, ontologies offer a promising approach to represent and manage knowledge about built environments, smart technologies, and user needs—especially within Ambient Assisted Living (AAL) systems. This paper presents a systematic literature review examining the role of ontologies in the reconfiguration of domestic living spaces, with a focus on their application in design processes and decision support systems. Following the PRISMA methodology, 14 relevant works published between 2000 and 2025 were identified and analyzed. The review explores key aspects such as ontology conceptualization, reuse, engineering methodologies, integration with CAD systems, and validation practices. The results show that research on this topic is fragmented yet growing, with the first contribution dated 2005 and peaks in 2016, 2018, and 2024. Most works (11) were conference papers, with Europe leading the contributions, particularly Italy. Half of the reviewed ontologies were developed “from scratch”, while the rest relied on conceptualizations such as BIM. Ontology reuse was inconsistent: only 50% of works reused existing models (e.g., SAREF, SOSA, BOT, ifcOWL), and few adopted Ontology Design Patterns. While 11 works followed ontology engineering methodologies—mostly custom or established methods such as Methontology or NeOn—stakeholder collaboration was reported in less than 36% of cases. Validation practices were weak: only six studies presented use cases or demonstrators. Integration with CAD systems remains at a prototypical stage, primarily through semantic enrichment and SWRL-based reasoning layers. Remaining gaps include poor ontology accessibility (few provide URLs or W3IDs), limited FAIR compliance, and scarce modeling of end-user needs, despite their relevance for AAL solutions. The review highlights opportunities for collaborative, human-centered ontology development aligned with architectural and medical standards to enable scalable, interoperable, and user-driven reconfiguration of domestic environments. Full article

In the architectural design phase, the inspection of design deliverables is critical, yet traditional manual checking methods are time-consuming, labor-intensive, and inefficient, with numerous drawbacks. With the development of BIM technology, automated rule compliance checking has become a trend. This paper presents a method combining semantic web technology and IFC data to enhance human–machine collaborative inspection capabilities. First, a five-step process integrated with domain specifications is designed to construct a building object ontology, covering most architectural objects in the AEC domain. Second, a set of mapping rules is developed based on the expression mechanisms of IFC entities to establish a semantic bridge between IfcOWL and the building object ontology. Then, by analyzing regulatory codes, query rule templates for major constraint types are developed using semantic web SPARQL. Finally, the feasibility of the method is validated through a case study based on the Jena framework. Full article

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning. Full article

Industry Foundation Classes (IFCs), as the most recognized data schema for Building Information Modeling (BIM), are increasingly combined with ontology to facilitate data interoperability across the whole lifecycle in the Architecture, Engineering, Construction, and Facility Management (AEC/FM). This paper conducts a bibliometric analysis of 122 papers from the perspective of data, model, and application to summarize the modes of IFC and ontology integration (IFCOI). This paper first analyzes the data and models of the integration from IFC data formats and ontology development models to the IfcOWL data model. Next, the application status is summed up from objective and phase dimensions, and four frequent applications with maturity are identified. Based on the aforementioned multi-dimensional analysis, three integration modes are summarized, taking into account various data interoperability requirements. Accordingly, ontology behaves as the representation of domain knowledge, an enrichment tool for IFC model semantics, and a linkage between IFC data and other heterogeneous data. Finally, this paper points out the challenges and opportunities for IFCOI in the data, domain ontology, and integration process and proposes a building lifecycle management model based on IFCOI. Full article

Intervention projects for historical buildings depend on the quality of multidisciplinary data sets; their collection, structure, and semantics. Building information model (BIM) based workflows for historical buildings accumulate some of the data sets in a shared information model that contains the building’s geometry assemblies with associated attributes (such as material). A BIM model of any building can be a source of data for different engineering assessments, for example, solar and wind exposure and seismic vulnerability, but for historic buildings it is particularly important for interventions like conservation, rehabilitation, and improvements such as refurbishment and retrofitting. When the BIM model is abstracted to a semantic model, enabling the use of semantic technologies such as reasoning and querying, semantic links can be established to other historical contexts. The semantic technologies help historic building experts to aggregate data into meaningful form. Ontologies provide them with an accurate knowledge representation of the concepts, relationships, and rules related to the historic building. In the paper, we are proposing an improved workflow for the transformation of a heritage BIM model to a semantic model. In the BIM part the workflow demonstrates how the fully parametric modelling of historical building components is relevant, for example, in terms of reusability and adaptation to a different context. In the semantic model part, ontology reuse, reasoning, and querying mechanisms are applied to validate the usability of the proposed workflow. The presented work will improve knowledge-sharing and reuse among stakeholders involved in historic building projects. Full article

Generally, building information modelling (BIM) models contain multiple dimensions of building information, including building design data, construction information, and maintenance-related contents, which are related with different engineering stakeholders. Efficient extraction of BIM data is a necessary and vital step for various data analyses and applications, especially in large-scale BIM projects. In order to extract BIM data, multiple query languages have been developed. However, the use of these query languages for data extraction usually requires that engineers have good programming skills, flexibly master query language(s), and fully understand the Industry Foundation Classes (IFC) express schema or the ontology expression of the IFC schema (ifcOWL). These limitations have virtually increased the difficulties of using query language(s) and raised the requirements on engineers’ essential knowledge reserves in data extraction. In this paper, we develop a simple method for automatic SPARQL (SPARQL Protocol and RDF Query Language) query generation to implement effective data extraction. Based on the users’ data requirements, we match users’ requirements with ifcOWL ontology concepts or instances, search the connected relationships among query keywords based on semantic BIM data, and generate the user-desired SPARQL query. We demonstrate through several case studies that our approach is effective and the generated SPARQL queries are accurate. Full article