Explanation Ontology: A Model of Explanations for User-Centered AI

Chari, Shruthi; Seneviratne, Oshani; Gruen, D.; Foreman, Morgan A.; Das, Amar K.; McGuinness, Deborah L.

doi:10.1007/978-3-030-62466-8_15

Cited by 44 publications

(43 citation statements)

References 15 publications

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“…To ensure objectivity when evaluating EduCOR, we decided to use inductive methods following [4,27] to select the most relevant evaluation criteria for our proposed ontology. Therefore, based on [2,12], we focus on coverage and adaptability as key performance indicators (KPIs) of the EduCOR ontology.…”

Section: Discussionmentioning

confidence: 99%

“…In this step of the ontology evaluation, we defined specific tasks and evaluated EduCOR's ability to fulfil them. For the task-based evaluation, we followed the approach of Chari et al [4], where competency questions are defined to reflect the main contributions of EduCOR, based on a sample use case that is expected to be executed by a potential user of the ontology. Such a use case is described as a general use case in Sect.…”

Section: Task-based Evaluationmentioning

confidence: 99%

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EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou

Abu-Rasheed

Tavakoli

et al. 2021

Lecture Notes in Computer Science

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With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

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Section: Discussionmentioning

confidence: 99%

Section: Task-based Evaluationmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou

Abu-Rasheed

Tavakoli

et al. 2021

Lecture Notes in Computer Science

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“…In big data-enabled, multidisciplinary geoscience research projects, interpretability of the workflow will be a big advantage for people from different disciplinary background to understand the result and finding (Reichstein et al, 2019). This overlaps with the work on explainable and meaningful Artificial Intelligence in computer science (Hagras, 2018;Holzinger, 2018;Chari et al, 2020). In the geoscience community, there has been some initial work on this topic in workflow platforms, such as the Meaning Spatial Statistics initiative (Stasch et al, 2014), and we anticipate that more works will appear in the near future.…”

Section: Big Data Smart Data Data Science and The Changes They Bring To Geosciencementioning

confidence: 99%

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Ma¹

2021

Preprint

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Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the current progress as well as the potential of data science for geoscience. To address that need, this article provides a review from the perspective of a data life cycle. The key steps in the data life cycle includes concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience of cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first is about the fundamental concepts and theoretical foundation of data science, and the second is the summary of highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is discussed, including topics on open science, smart data, and science of team science. We hope this review will be useful to data science practitioners in the geoscience community, and will lead to more discussions on the best practices and future trends of data science for geoscience.

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Section: Task-based Evaluationmentioning

confidence: 99%

“…Furthermore, recent works revealed the increased interest in educational Knowledge Graphs [10,20], which, however, often lack an underlying ontology or schema [5]. Commercial products seem to follow a similar direction, as they often do not use or do not publish their underlying knowledge schema 4 . Additionally, surveys in e-learning have shown that an ontology is helpful in achieving personalised recommendation systems [17,31].…”

Section: Introductionmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou,

Abu-Rasheed,

Tavakoli

et al. 2021

Preprint

View full text Add to dashboard Cite

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multisource learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user's learning goals, academic and psychological parameters, and the labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the reallife learning platform eDoer is discussed as a use-case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.

show abstract

Explanation Ontology: A Model of Explanations for User-Centered AI

Cited by 44 publications

References 15 publications

EduCOR: An Educational and Career-Oriented Recommendation Ontology

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

EduCOR: An Educational and Career-Oriented Recommendation Ontology

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