An Ontology-Based Mediation Framework for Integrating Federated Sources of BIM and IoT Data

Shahinmoghadam, Mehrzad; Motamedi, Ali

doi:10.1007/978-3-030-51295-8_63

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…To deal with the prevalent heterogeneity and dynamic nature of the data sources, the wrappers access each data source, retrieving and exporting data along with the source data schema, while the mediator transforms the retrieved data into a unified and global data schema that is sufficiently rich to accommodate heterogeneous data. For instance, Shahinmoghadam and Motamedi [14] proposed an ontology-based mediation mechanism to realise the integration of BIM and IoT data, by rewriting queries with reference to the ontology mapping between the entities of the BIM or IoT ontology-based schemas and the global ontology-based schema. Essentially, it is close to the system integration approach, in which individual systems are combined through APIs.…”

Section: Data Warehouse Approachmentioning

confidence: 99%

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Xie

Moretti

Merino

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.

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Section: Data Warehouse Approachmentioning

confidence: 99%

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Xie

Moretti

Merino

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…In particular, various machine learning algorithms can be applied over the learnt semantic embeddings of the various schemas used in AEC/FM domains to find the semantic correspondences of the entities between two or more schemas. Such application will be of high value to the integration of the disparate sources of the building data (e.g., BIM and sensor data integration (Shahinmoghadam and Motamedi, 2020)). As another important application, knowledge graph embeddings can be used to facilitate the semantic enrichment of the building information models.…”

Section: Potential Benefits and Applicationsmentioning

confidence: 99%

“…The most common tools and technologies that were used in previous studies are among the ones that are officially recommended by W3C consortium as the semantic web standardized specifications, namely: OWL (web ontology language), RDF (Resource Description Framework), and SPARQL (a semantic query language for RDF triples). The ifcOWL ontology, an OWL-based representation of the Industry Foundation Classes (IFC) schema (ifcOWL, no date), has been commonly used to enable extra features (e.g., automated reasoning, highly expressive semantic query) for extending the general capabilities of Building Information Modeling (BIM), e.g., enhancing BIM interoperability with sensory operational data as previously reported in (Terkaj, Schneider and Pauwels, 2017;Zhong et al, 2018;Shahinmoghadam and Motamedi, 2020).…”

Section: Introductionmentioning

confidence: 99%

Enabling downstream machine-learning over the textual information contained in building knowledge graphs

Mehrzad¹,

Ali²,

Mostafa³

2022

Proceedings of the 2022 European Conference on Computing in Construction

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The current practice of statistical learning from building information models mostly relies on the manual construction of table-oriented representation of the numeric data. This is while the lexical information contained in building information models can further reinforce the learning process by preserving the essential role of the semantic relationships. In this paper, application of one of the state-of-the-art knowledge graph embedding algorithms (RDF2Vec), yielded promising results with regards to an object clustering task. This paper contributes to the literature by shedding light on the potential of semantic embedding algorithms to facilitate downstream machine learning over building knowledge graphs.

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“…Nevertheless, these platforms, in the absence of a real-time connection to the model, primarily operate as conventional document management systems and do not embody the concept of a common data environment. Several researchers [25,26] are focusing on the challenges of integrating BIM and data from the Internet of Things (IoT), which could bring a paradigm shift to the construction industry by improving efficiency and transparency in an unprecedented manner. However, without standards or guidelines for information integration and data management, organizing large and heterogeneous data sets into a usable format is both costly and time-consuming.…”

mentioning

confidence: 99%

Enhancing BIM Integration: A Comparative Analysis of Novel Composite Structure Documentation Methods

Szalai,

Ajtayné Károlyfi

2024

Buildings

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The proper selection and planning of building materials are crucial tasks in architectural design, as they fundamentally impact the functioning of the structure. In traditional design processes, this information is recorded in text form, typically using word processing software. However, this approach hinders the integration with modern, data-driven design methods and is incompatible with the increasingly popular building information modeling (BIM) processes. To address this, two new methods have been developed: one database-like method in the form of an Excel spreadsheet and the other as a dedicated web application. This article introduces and compares these methods based on pilot projects conducted by university students and an expert. Based on the results of the study conducted among students and expert, the database-like method proves to be the fastest. For students, creating a composite structure took an average of 14–20 min, while for experts, it took an average of 1.2 min. According to the evaluation of participants, the traditional method does not facilitate automatic communication with BIM, while both the database and web solutions promote it. The web-based solution, with its dedicated layout and functionality, offers additional advantages in this regard. The research underscores the importance of structured data in BIM and proposes new methods to streamline composite documentation processes during the design phase.

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An Ontology-Based Mediation Framework for Integrating Federated Sources of BIM and IoT Data

Cited by 8 publications

References 24 publications

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

Enabling downstream machine-learning over the textual information contained in building knowledge graphs

Enhancing BIM Integration: A Comparative Analysis of Novel Composite Structure Documentation Methods

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