A review of ontologies for smart and continuous commissioning

• URL: http://arxiv.org/abs/2205.07636v1
• Date: Wed, 11 May 2022 13:59:45 GMT
• Title: A review of ontologies for smart and continuous commissioning
• Authors: Sara Gilani, Caroline Quinn, J.J. McArthur (Faculty of Engineering and Architectural Science, Ryerson University, Toronto, Canada)
• Abstract summary: Ontologies play an important role in continuous commissioning ( SCCx) of buildings as they facilitate data and reasoning by machines. This paper reviews the state-of-the-art research on building data since 2014 within the SCCx domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Smart and continuous commissioning (SCCx) of buildings can result in a significant reduction in the gap between design and operational performance. Ontologies play an important role in SCCx as they facilitate data readability and reasoning by machines. A better understanding of ontologies is required in order to develop and incorporate them in SCCx. This paper critically reviews the state-of-the-art research on building data ontologies since 2014 within the SCCx domain through sorting them based on building data types, general approaches, and applications. The data types of two main domains of building information modeling and building management system have been considered in the majority of existing ontologies. Three main applications are evident from a critical analysis of existing ontologies: (1) key performance indicator calculation, (2) building performance improvement, and (3) fault detection and diagnosis. The key gaps found in the literature review are a holistic ontology for SCCx and insight on how such approaches should be evaluated. Based on these findings, this study provides recommendations for future necessary research including: identification of SCCx-related data types, assessment of ontology performance, and creation of open-source approaches.

Related papers

• A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis [8.629569588488328]
  This paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focus on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area.
  arXiv  Detail & Related papers  (2024-10-22T19:12:03Z)
• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance [41.85920785319125]
  The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. In phase two, CPS life cycle data is contextualized using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the Cross-Industry Standard Process for Data Mining (CRISP-DM) to efficiently extract new insights from the data.
  arXiv  Detail & Related papers  (2024-07-09T15:06:47Z)
• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Systematic Analysis of COVID-19 Ontologies [5.286727853896068]
  The study is conducted through a dual-stage approach, commencing with a systematic review of relevant literature. Twenty-four COVID-19 Ontologies (CovOs) are selected and examined. The METHONTOLOGY approach emerges as a favored design methodology, often coupled with application-based or data-centric evaluation methods.
  arXiv  Detail & Related papers  (2023-09-15T18:17:01Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections [5.8962650619804755]
  Inferring semantic relations between related scientific concepts is a crucial step. BERT-based pre-trained models have been popularly explored for automatic relation classification. Existing methods are primarily evaluated on clean texts. To address these limitations, we started by creating OCR-noisy texts.
  arXiv  Detail & Related papers  (2023-05-03T17:32:16Z)
• Detection and Evaluation of Clusters within Sequential Data [58.720142291102135]
  Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees. In particular, our sequential data is derived from human DNA, written text, animal movement data and financial markets. It is found that the Block Markov Chain model assumption can indeed produce meaningful insights in exploratory data analyses.
  arXiv  Detail & Related papers  (2022-10-04T15:22:39Z)
• Computer vision-based analysis of buildings and built environments: A systematic review of current approaches [0.98314893665023]
  This paper presents a first systematic review of the computer vision-based analysis of buildings and the built environments. It reveals current research gaps and trends, and highlights two main categories of research aims.
  arXiv  Detail & Related papers  (2022-08-01T14:17:51Z)
• Wizard of Search Engine: Access to Information Through Conversations with Search Engines [58.53420685514819]
  We make efforts to facilitate research on CIS from three aspects. We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS) and response generation (RG) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
  arXiv  Detail & Related papers  (2021-05-18T06:35:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.