使用智能手表应用中实现的乘员反馈本体创建以乘员为中心的数字孪生

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Semantic Web Pub Date : 2022-11-08 DOI:10.3233/sw-223254
Alex Donkers, B. de Vries, Dujuan Yang
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引用次数: 3

摘要

居住者的反馈使建筑管理者能够改善居住者的健康、舒适和满意度。然而,获取持续的居住者反馈并将这些反馈与其他建筑信息整合是具有挑战性的。本文提出了一种可扩展的方法来获取持续的居住者反馈,并直接将其与其他建筑信息相结合。语义网技术被用于解决数据互操作性问题。开发了乘员反馈本体来对反馈进行语义描述。除此之外,他们还开发了一款名为mental的智能手表应用程序,用于获取室内环境质量的持续反馈。该应用程序收集位置、医疗信息,并回答简短的微调查。Mintal应用了居住者反馈本体,直接将反馈与相关的建筑数据相结合。通过案例研究对该方法进行了评价。通过整合相关的建筑数据、传感器数据和居住者反馈,创建了一个语义数字双胞胎。SPARQL查询的结果更深入地了解了开放式公寓中居住者的舒适度。案例研究展示了如何将反馈与建筑信息相结合,从而实现更多以乘员为中心的决策支持工具。本文中提出的方法可以在广泛的用例中使用,既可以在体系结构、构建和构造领域内,也可以不在体系结构领域内。
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Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app
Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
期刊最新文献
Using Wikidata lexemes and items to generate text from abstract representations Editorial: Special issue on Interactive Semantic Web Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1 Special Issue on Semantic Web for Industrial Engineering: Research and Applications Declarative generation of RDF-star graphs from heterogeneous data
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