X3D Ontology for Querying 3D Models on the Semantic Web

Brutzman, Don; Flotyński, Jakub

doi:10.1145/3424616.3424715

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…40,41 The light weighted and efficient architecture can reduce the processing time of the model and further increase the transparency of the BIM applications, which are essential aspects when performing semantic induction since the delay of immediate model presentation can cause potential risk and result in outdated knowledge management. Another potential application of the suggested framework is the combination of the cloud-based lightweight processing mechanism with semantic web technology 42,43 and X3D semantic web 44,45 to better visualize large 3D models. The domain knowledge and expertise from specific web ontologies can be further integrated into the CEBOW framework in order to selectively render the model partially.…”

Section: Discussionmentioning

confidence: 99%

CEBOW: A Cloud‐Edge‐Browser Online Web3D approach for visualizing large BIM scenes

Zhao

Zhang

et al. 2022

Computer Animation & Virtual

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With the mobile technology continues to grow and evolve, the technology of presenting building information modeling (BIM) with an online platform has become an important application in the fields of civil engineering, architecture, and computer visualization. However, due to network bandwidth and browser performance limitations, it was difficult to display large‐scale BIM scenes in a flawless manner on mobile browsers. CEBOW, a Cloud‐Edge‐Browser Online architecture for visualizing BIM components with online solutions, is proposed in this article. The method combines transmission scheduling, cache management, and optimal initial loading into a single system architecture. For network transmission testing, BIM scenes are used, and the results show that our method effectively reduces scene loading time and networking delay while improving the visualization effect of large‐scale scenes.

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Section: Discussionmentioning

confidence: 99%

CEBOW: A Cloud‐Edge‐Browser Online Web3D approach for visualizing large BIM scenes

Zhao

Zhang

et al. 2022

Computer Animation & Virtual

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“…We also cannot represent the objects' states, affordances, and attributes. The first problem was solved using the Time Ontology 8 and X3D ontology 9 [40]. For the second problem, we defined new classes and properties for these representations.…”

Section: Schema Designmentioning

confidence: 99%

Synthesizing Event-Centric Knowledge Graphs of Daily Activities Using Virtual Space

et al. 2023

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“…After studying some of the existing and commonly used domain vocabularies, including ISO 10303, 1 ISO 15926 (Klüwer et al, 2008 ), X3D ontology (Brutzman and Flotyński, 2020 ), the XMANAI graph data model has been defined re-using their core concepts for manufacturing, and is used in various steps of the XAI pipelines to add semantics to the data. The XMANAI Data Model describes the nodes, their properties and relationships.…”

Section: The Xmanai Approach—explainable Data-models-servicesmentioning

confidence: 99%

Explainability as the key ingredient for AI adoption in Industry 5.0 settings

Agostinho,

Dikopoulou,

Lavasa

et al. 2023

Front. Artif. Intell.

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Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the “transparency paradox” of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.

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X3D Ontology for Querying 3D Models on the Semantic Web

Cited by 4 publications

References 29 publications

CEBOW: A Cloud‐Edge‐Browser Online Web3D approach for visualizing large BIM scenes

CEBOW: A Cloud‐Edge‐Browser Online Web3D approach for visualizing large BIM scenes

Synthesizing Event-Centric Knowledge Graphs of Daily Activities Using Virtual Space

Explainability as the key ingredient for AI adoption in Industry 5.0 settings

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