A top-level ontology for smart environments

Ye, Juan; Stevenson, Graeme; Dobson, Simon

doi:10.1016/j.pmcj.2011.02.002

Cited by 59 publications

(25 citation statements)

References 31 publications

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“…They are aimed at unifying concepts and terminologies among members of a certain group who need to share information (Bimba et al, 2016). However, generic ontologies are valid across multiple domains (Ye, Stevenson, & Dobson, 2011). They represent various concepts such as event, state, process, action, etc.…”

Section: Knowledge Base Modeling Approachesmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

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Section: Knowledge Base Modeling Approachesmentioning

confidence: 99%

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Al-Hunaiyyan

Bimba

Idris

et al. 2017

IJIKM

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“…Other tools used are Goal-Oriented Application Ontology Development Technique "GAODT" (Santos et al, 2013), Knowledge Modelling System (KMS) (Chan, 2004a) and Model Driven Architecture (MDA) (Santoso et al, 2011). The common programming language used in application ontology development is the Web Ontology Language (OWL) (Durbha et al 2009;Ye et al, 2011). However, Resource Definition Framework (RDF) and OWL have been combined to convert cause-effect relationships of a concept while developing application ontologies (Ebrahimipour & Yacout, 2015).The major challenge of application ontologies is its reusability (Van Heijst et al, 1997).…”

Section: Application Ontologiesmentioning

confidence: 99%

“…Generic ontologies sometimes referred to as top-level ontologies, are usually valid over various domains (Santoso et al, 2011;Xing et al, 2009;Ye et al, 2011). They define concepts like state, event, process, action, component etc.…”

Section: Generic Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…For example, a sensor event can be represented as [2008-02-25T00:20:14Z, bedroom, door, main user], indicating that the sensor installed at the door of the bedroom that belongs to the main user fires at the give timestamp. We adopt an ontological approach where we organise concepts in each feature space into a hierarchy based on their granularity level [34]. In the above example, bedroom, door, and main user are concepts or instances in the Location, Object, and User feature space, and their relationships with the other peer concepts can be: bedroom sleeping area living environment, door movable structure (from WordNet [22]), and main user any resident.…”

Section: Distance Measures Between Sensor Eventsmentioning

confidence: 99%

Detecting abnormal events on binary sensors in smart home environments

Stevenson

Dobson

2016

Pervasive and Mobile Computing

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With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.

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A top-level ontology for smart environments

Cited by 59 publications

References 31 publications

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

A Cognitive Knowledge-based Framework for Social and Metacognitive Support in Mobile Learning

Towards knowledge modeling and manipulation technologies: A survey

Detecting abnormal events on binary sensors in smart home environments

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