DABGEO: A reusable and usable global energy ontology for the energy domain

Cuenca, Javier; Larrinaga, Félix; Curry, Edward

doi:10.1016/j.websem.2020.100550

Cited by 14 publications

(7 citation statements)

References 22 publications

(84 reference statements)

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“…Despite the fact that the proposed ontologies differ in how exactly they represent building subcomponents and their relationships, most of them are extensible and compatible with one another. Further perspectives on buildings are provided by specific domain ontologies, such as the iCity Building ontology (Katsumi, 2021) providing a foundational vocabulary to represent building-related data within the urban context or the Domain Analysis-Based Global Energy Ontology (DABGEO) (Cuenca et al, 2020) fostering interoperability across the energy domain, with a focus on smart home and smart city energy management (Cuenca et al, 2020). The DABGEO ontology (Cuenca and Larrinaga, 2019) contains a tailored sub-module for knowledge about infrastructure and buildings, containing classes, properties, and axioms to represent static building features (e.g., surface, material), geometrical details (rooms, floors) as well as internal and external environmental conditions (e.g., room temperature).…”

Section: Building Ontologiesmentioning

confidence: 99%

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Hofmeister,

Bai,

Brownbridge

et al. 2024

DCE

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This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.

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Section: Building Ontologiesmentioning

confidence: 99%

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Hofmeister,

Bai,

Brownbridge

et al. 2024

DCE

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“…Although ontologies may contain relatively generalisable information that allows them to be reused for different purposes [Spyns et al, 2002], they are often employed in NLP tasks that require specialised knowledge and vocabulary [Khadir et al, 2021]. Some studies have introduced ontologies developed for the energy sector; for example, [Booshehri et al, 2021], [Cuenca et al, 2020], and [Küçük and Arslan, 2014] for English, and [Glenc, 2022] for Polish.…”

Section: An Anonymous Commenter On An Online News Site In January 2023mentioning

confidence: 99%

Applying computational approaches to energy discourse: a comparative methodological study of rule-based and large language model qualitative content analysis

Kousa

2024

Journal of Data Mining &Amp; Digital Humanities

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The objective of this study is to conduct a comparative analysis of a rule-based ontological classification tool and a large language model (LLM) chatbot as qualitative content analysis tools. The focus is on assessing their strengths and limitations when applied to the study of the discourse surrounding the energy transition. To achieve this, I used the tools to analyse two different types of corpora: citizens' social media discussions and politicians' parliamentary speeches. In the analysis, I evaluated the differences in the methods' recall and precision levels. Additionally, I assessed the extent to which these methods align with scientific principles, including reliability, transparency, and research integrity. The results reveal a classic trade-off: LLM's precision is high, but its recall is comparatively low, suggesting its strength in generating accurate but potentially incomplete analyses. On the other hand, the rule-based method outperforms in recall at the expense of precision, capturing more data points, but with varying accuracy levels. I discuss the implications of the results and outline ideas for leveraging the strengths of both methods in future studies. This article provides researchers with insights into the selection and application of computational tools in the social sciences and humanities, as well as in multifaceted research topics, such as energy transition.

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“…However, Marquardt and Schneider also acknowledged that even with the ontology, it is unrealistic for tools that have different interfaces, have inputs and outputs of different syntax, and require different area of expertise to interoperate at large scale 125 . Furthermore, OntoCAPE, while being the most widely used ontology in PSE, 141 has not been extensively tested in PSE research, although researchers use OntoCAPE as the basis to develop ontologies of their own 147,148 . The second key takeaway from Shobrys and White 146 is that systems integration is not a technical issue, but an organizational one.…”

Section: Process Systems Engineeringmentioning

confidence: 99%

“…125 Furthermore, OntoCAPE, while being the most widely used ontology in PSE, 141 has not been extensively tested in PSE research, although researchers use OntoCAPE as the basis to develop ontologies of their own. 147,148 The second key takeaway from Shobrys and White 146 is that systems integration is not a technical issue, but an organizational one. They revealed the communication gaps, or "seams," often form between departments within a company.…”

Section: Challenges To Model-based Process Systems Engineeringmentioning

confidence: 99%

Model‐based systems engineering approaches to chemicals and materials manufacturing

Le,

Feingold,

Glandorf

et al. 2023

AIChE Journal

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Model‐based systems engineering (MBSE) is part of a long‐term trend toward model‐centric approaches adopted by many engineering disciplines. We establish the need of an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. We discuss the origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) and discuss limitations of the Process Systems Engineering (PSE) framework. We make the case for MBSE as a more generalizable approach. We introduce systems modeling strategies for MBSE, and a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. We demonstrate a specific use case of this method by creating a systems model for the manufacturing of an active pharmaceutical ingredient, Atropine. We conclude with a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains.

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DABGEO: A reusable and usable global energy ontology for the energy domain

Cited by 14 publications

References 22 publications

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Applying computational approaches to energy discourse: a comparative methodological study of rule-based and large language model qualitative content analysis

Model‐based systems engineering approaches to chemicals and materials manufacturing

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