An Ontological Approach for Domain Knowledge Modeling and Management in E-Learning Systems

Panagiotopoulos, Ιoannis; Kalou, Aikaterini K.; Pierrakeas, Christos; Kameas, Achilles

doi:10.1007/978-3-642-33412-2_10

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…The procedure of requesting the importOntology service is presented in Figure 6. Considering the treatment of replicas, as in the example given in [50], concepts with equal names are removed. However, while importing two ontologies with similar concepts and applying unification procedures, the resulting concept class possesses all properties from the initial ontologies.…”

Section: Import Ontology Servicementioning

confidence: 99%

Semantic Data Management for a Virtual Factory Collaborative Environment

et al. 2019

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Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.

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Section: Import Ontology Servicementioning

confidence: 99%

Semantic Data Management for a Virtual Factory Collaborative Environment

et al. 2019

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“…Today, the most widely used knowledge representation models are: a logical model, a production model, a model in the form of semantic network, a frame model, an ontological model [21][22][23]. The knowledge representation languages (KRL) are used for development of DSS, based on the specified models [24].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Organization of information support for business processes at aviation enterprises by means of ontological engineering

Shostak

Danova

Romanenkov

et al. 2018

EEJET

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“…It is worth noting here that the process for the cognitive domain representation from which we construct the corresponding learning objects and also the definition of the learning outcomes for the different cognitive domains is realized within a welldefined and applied collaborative methodology between domain experts (tutors) and knowledge engineers, described in (Panagiotopoulos et al, 2012, Nikolopoulos et al, 2013.…”

Section: Learning Objects and Outcomesmentioning

confidence: 99%

Combining Agents and Ontologies for Building an Intelligent Tutoring System

Panagiotis

Panagiotopoulos

Goumopoulos

et al. 2015

Proceedings of the 7th International Conference on Computer Supported Education

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In this paper an approach for building an intelligent tutoring system is presented, based on a multi-agent architecture and combined with ontologies for knowledge representation. The system developed is focused on a bottom up, reactive generation of an active sequence of knowledge units regarding a set of adjustable, high level learning goals. The learning process begins with a set of simple learning goals that require a few learning objects and as the educational process proceeds, the student has to achieve higher learning outcomes that combine other low level outcomes which have been already achieved. The system is able to adapt to student's learning profile and progress by applying proper learning tactics to prioritize through a weight calculation scheme the sequence of the learning outcomes to achieve. The main components of the system consisting of ontological models of the learner and the subject under study, gateway agents and tutor agents with their core modules (learning space management and learning tactics control) are explained and a detailed description of their interaction is given in the context of an example application. Finally, the advantages of the proposed approach are laid out, especially in the setting of a distance learning education system.

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An Ontological Approach for Domain Knowledge Modeling and Management in E-Learning Systems

Cited by 11 publications

References 9 publications

Semantic Data Management for a Virtual Factory Collaborative Environment

Semantic Data Management for a Virtual Factory Collaborative Environment

Organization of information support for business processes at aviation enterprises by means of ontological engineering

Combining Agents and Ontologies for Building an Intelligent Tutoring System

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