Ontology Usability Scale: Context-aware Metrics for the Effectiveness, Efficiency and Satisfaction of Ontology Uses

Ma, Xiaogang; Fu, Linyun; West, Patrick; Fox, Peter

doi:10.5334/dsj-2018-010

Cited by 11 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also worth mentioning that even when comparing two ontology-based approaches based on the same knowledge, attention should not be focused on the result of the classification, but on other characteristics of the ontology. Different metrics can be used to assess the quality of the developed ontology (Ma et al 2018) including structural metrics (formalization, structural accuracy, consistency, cohesion, domain coverage), functional adequacy (controlled vocabulary, inference, precision etc. ), compatibility (portability and adaptability), operability (appropriateness, or ease of use), maintainability (modularity, reusability etc.)…”

Section: How Do the Ontologies Improve The Classification Results?mentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

View full text Add to dashboard Cite

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts' expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

show abstract

Section: How Do the Ontologies Improve The Classification Results?mentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Therefore, to be suitable for ontology evaluation the scale needed to be modified to address the specifics of the task at hand. Previous work has already demonstrated that a modified version of SUS can be used to evaluate the usability of ontologies [21], [23], [24]. In the present work, we adhered to the SUS modification for ontology evaluation described in [21].…”

Section: Cqs-basedmentioning

confidence: 99%

Ontology-Driven Knowledge Sharing in Alzheimer's Disease Research

Lazarova¹,

Petrova-Antonova²,

Kunchev³

2023

Preprint

View full text Add to dashboard Cite

Alzheimer’s disease is a debilitating neurodegenerative condition which is known to be the most common cause of dementia. Despite its rapidly growing prevalence, medicine still lacks a comprehensive definition of the disease. As a result, Alzheimer’s disease remains neither preventable nor curable. In recent years, broad interdisciplinary collaborations in Alzheimer’s disease research are becoming more common. Furthermore, such collaborations have already demonstrated their superiority in addressing the complexity of the disease in innovative ways. However, establishing effective communication and optimal knowledge distribution between researchers and specialists with different expertise and background is not a straightforward task. To address this challenge, we propose the Alzheimer’s disease Ontology for Diagnosis and Preclinical Classification (AD-DPC) as a tool for effective knowledge sharing in interdisciplinary/multidisciplinary teams working on Alzheimer’s disease. It covers six major conceptual groups, namely Alzheimer's disease pathology, Alzheimer's disease spectrum, Diagnostic process, Symptoms, Assessments, and Relevant clinical findings. All concepts were annotated with definitions or elucidations and in some cases enriched with synonyms and additional resources. The potential of AD-DPC to support non-medical experts is demonstrated through an evaluation of its usability, applicability and correctness. The results show that the participants in the evaluation process who lack prior medical knowledge can successfully answer Alzheimer’s disease-related questions by interacting with AD-DPC. Furthermore, their perceived level of knowledge in the field increased leading to effective communication with medical experts.

show abstract

“…The development and the implementation of MetaOntology as discussed in the previous section is followed by ontology evaluation. There are several ontology assessment metrics that have been reported in the literature (Alani and Brewster, 2006 ; Dellschaft and Staab, 2008 ; d'Aquin et al, 2009 ; Yu et al, 2009 ; Zavitsanos et al, 2010 ; Ma et al, 2018 ). Ontology evaluation determines an ontology's quality and if its limitations and criteria have been satisfied.…”

Section: Metaontology: Ontology Design For the Metaversementioning

confidence: 99%

“…This study also utilizes Ontology Usability Scale (OUS) (Ma et al, 2018 ) to further examine the usability of MetaOntology by considering feedback from domain experts. The intuition behind OUS has been derived from the System Usability Scale metric (Brooke, 1996 ) which is a ten-item Likert scale, with five response options; from Strongly disagree (1) to Strongly agree (5), that is commonly used to establish a metric to measure the usability of products and services.…”

Section: Metaontology: Ontology Design For the Metaversementioning

confidence: 99%

MetaOntology: Toward developing an ontology for the metaverse

Abu-Salih

2022

Front. Big Data

View full text Add to dashboard Cite

Metaverse is now perceived as a celebrated future version of the internet. In this new anticipated virtual universe, interconnected digital platforms leveraged by augmented, extended, and virtual realities will elevate users' immersive experiences through multidimensional interactions. In particular, users will be offered a broad spectrum of digital activities within a newly immersive setting mediated by technology. This study aims to design a domain ontology (MetaOntology) for the metaverse to provide an explicit specification of relevant state-of-the-art technologies and infrastructure. A four-step methodological approach is followed to construct the designated ontology. Due to the immaturity of the metaverse, MetaOntology is not intended to furnish a complete outlook on the domain, rather it aims to establish a cornerstone so as to facilitate future efforts in building extant versions of this ontology considering the evolvement of relevant technologies.

show abstract

Ontology Usability Scale: Context-aware Metrics for the Effectiveness, Efficiency and Satisfaction of Ontology Uses

Cited by 11 publications

References 11 publications

Ontologies to interpret remote sensing images: why do we need them?

Ontologies to interpret remote sensing images: why do we need them?

Ontology-Driven Knowledge Sharing in Alzheimer's Disease Research

MetaOntology: Toward developing an ontology for the metaverse

Contact Info

Product

Resources

About