Towards the abstract system theory of system science for cognitive and intelligent systems

Wang, Yingxu

doi:10.1007/s40747-015-0001-5

Cited by 34 publications

(20 citation statements)

References 83 publications

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“…Knowledge in the cognitive knowledge base is manipulated as a dynamic concept network, similar to human knowledge processing (Bimba et al, 2016). A cognitive unit represents concepts which identify and model both concrete and abstract entities (Wang, 2015b).…”

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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“…Based on the fundamental theories of knowledge base modelling and manipulation, knowledge base technology can be categorized into four groups: 1) the linguistic knowledge bases (Baker, 2014;Fellbaum, 1998;Speer & Havasi, 2012); 2) expert knowledge bases (Driankov et al, 2013;Kerr-Wilson & Pedrycz, 2016;Kung & Su, 2007); 3) ontology (Fensel, 2004;Sánchez, 2010;Studer et al, 1998;Van Heijst et al, 1997) and most recently 4) the cognitive knowledge base (Wang, 2015b). The various categories and types of knowledge base modelling figure 2.…”

Section: Classification Of Knowledge Base Modelling Techniquesmentioning

confidence: 99%

“…It classifies these technologies according to their development theories and structure, resulting to four categories; the linguistic knowledge bases (Collin F Baker, 2014;Fellbaum, 1998;Speer & Havasi, 2012), expert knowledge bases (Driankov, Hellendoorn, & Reinfrank, 2013;Kerr-Wilson & Pedrycz, 2016;Kung & Su, 2007), ontology (Khan, 2009;Fensel, 2004;David Sánchez, 2010;Studer etal., 1998;Van Heijst et al, 1997) and most recently the cognitive knowledge base (Wang, 2015b). Human knowledge is categorized at the levels of data, information, knowledge and intelligence.…”

Section: Introductionmentioning

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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“…[22] Recent tutoring systems that deal with emotionally-charged social situations are particularly compelling and increasingly fascinating. [23] An intelligent system with embedded elements may be dominant enough to analyse data but basically is specialized for tasks related to the host machine. It present all devices such as smart meters, digital televisions, automobiles, traffic lights, digital signage, airplane controls and Point-of-Sale (POS) terminals, among a large number of other possibilities.…”

Section: Expert System Based Artificialmentioning

confidence: 99%

Artificial Intelligence Technology and its Challenges-A Review

Abitha¹

2016

JECSE

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In the last two decades, researchers have found many advantages related to Artificial Intelligence (AI) in the performance of both service and manufacturing systems. It includes alternate methods to conventional techniques and there are mechanisms of integrated systems. Currently, many researches related to artificial intelligence are under process. At present, artificial intelligence has been used to solve various problems in several fields and has become more popular around the world. This field is extremely difficult to review either chronologically or thematically. AI has a long history of growing constantly and changing tremendously. Connected with the history of computers, artificial intelligence is the main concept behind the fifth generation computers. This review paper deals with the framework of artificial intelligence and focuses on fields related to artificial intelligence. Particularly this paper describes about the recent growth in the field of artificial intelligence and its applications. Several issues of artificial intelligence are analysed. It is concluded that still many researches are yet to be conducted for further growth in the field of Artificial Intelligence (AI).

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Towards the abstract system theory of system science for cognitive and intelligent systems

Cited by 34 publications

References 83 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

Artificial Intelligence Technology and its Challenges-A Review

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