The RACE system is a knowledge representation system that implements a highly optimized tableaux calculus for an expressive description logic. It offers reasoning services for multiple TBoxes and for multiple ABoxes as well. The system implements the description logic ALCNHR+. This is the basic logic ALC augmented with number restrictions, role hierarchies and transitive roles. RACE supports the specification of general terminological axioms. A TBox may contain general concept inclusions (GCIs), which state the subsumption relation between two concept terms. Multiple definitions or even cyclic definitions of concepts can be handled by RACE.

RACE User''s Guide and Reference Manual

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Conference on Artificial Intelligence in Education

With current advancements in the modelling and simulation of systems and networks, researchers and developers are better able to determine the probable state of current systems and envision the state of future systems during the design stage. The uses and accuracies of these models are essential to every aspect of communication systems.

Integrated Models for Information Communication Systems and Networks: Design and Development explores essential information and current research findings on information communication systems and networks. This reference source aims to assist professionals in the desire to enhance their knowledge of modelling at systems level with the aid of modern software packages.

Integrated Models for Information Communication Systems and Networks: Design and Development

Ontologies are used with great success in education because they allow to formulate the representation of a learning domain by specifying all concepts involved, relations between concepts and all properties and conditions that exist. The goal of this paper is to present the field of ontologies and give an overview of recent research in the field, in the context of education. As this paper presents a literature review, papers from the last five years were collected from the IEEE Xplore database, analysed and categorized based on the use of ontologies for: curriculum modelling and management, describing learning domains, learning data, and e-learning services. From the collected papers, a slightly growing trend in the contribution of ontologies to educational systems can be observed. Most studies used ontologies for describing learning domains, and some of the 95 collected papers could not fit in just one category because a system used more than one ontology. Throughout the work, the following contributions have been made: the term ontology was defined, the most common types of ontologies and commonly used methodologies for building ontologies were identified, and an overview of existing systems that use ontologies in the domain of education was given.

Ontologies in education – state of the art

A Novel Ontology Consistent with Acknowledged Standards in Smart Homes

This paper presents an ontology for an e-Learning Management System (LMS), which arranges metadata, and defines the relationships of metadata, which are about learning objects; belong to academic courses and user profiles. This ontology has been incorporated as a critical part of the proposed architecture. By this ontology, effective retrieval of learning content, customizing LMS is expected. Metadata used in this paper are based on current metadata standards. This ontology specified in human and machine-readable formats. In implementing it, several APIs were defined to manage the ontology. They were introduced into a typical open sourced LMS. Proposed ontology maps user preferences with learning content to satisfy learner requirements. These learning objects are presented to the learner based on ontological relationships. Hence it increases the usability and customizes the LMS.

A Learner Oriented Ontology Of Metadata To Improve Effectiveness Of Learning Management Systems.

- There is a boost in the interest on ontology with the developments in Semantic Web technologies. Ontologies play a vital role in semantic web. Even though there is lot of work done on ontology, still a standard framework for ontology engineering has not been defined. Even though current ontology engineering methodologies are available they need improvements. The effort of our work is to integrate various methods, techniques, tools and etc to different stages of proposed ontology engineering life cycle to create a comprehensive framework for ontology engineering. Current methodologies discuss ontology engineering stages and collaborative environments with user collaboration. However, discussion on increasing effectiveness and correct inference has been given less attention. More over, these methodologies provide little discussion on usability of domain ontologies. We consider these aspects as more important in our work. Also, ontology engineering has been done for various domains and for various purposes. Our effort is to propose an iterative and incremental approach for ontology engineering especially for e-learning domain with the intention of achieving a higher usability and effectiveness of e-learning systems. This paper introduces different aspects of the proposed ontology engineering framework and evaluation of it.

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An Iterative and Incremental Approach for E-Learning Ontology Engineering

Many experimental ontologies have been developed for the learning domain for use at different institutions. These ontologies include different OWL/OWL 2 (Web Ontology Language) constructors. However, it is not clear which OWL 2 constructors are the most appropriate ones for designing ontologies for the learning domain. It is possible that the constructors used in these learning domain ontologies match one of the three standard OWL 2 profiles (sublanguages). To investigate whether this is the case, we have analysed a corpus of 14 ontologies designed for the learning domain. We have also compared the constructors used in these ontologies with those of the OWL 2 RL profile, one of the OWL 2 standard profiles. The results of our analysis suggest that the OWL 2 constructors used in these ontologies do not exactly match the standard OWL 2 RL profile, but form a subset of that profile which we call OWL 2 Learn.

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OWL 2 learn profile: an ontology sublanguage for the learning domain

Current e-learning systems are database-driven; this makes it hard for users to find information in a direct way using the domain terminology they are familiar with from daily use. In order to alleviate this problem we propose the use of a learning ontology that uses an intuitive naming convention and combines learning concepts with user profiles and learning object metadata (LOM) for the easy access of learning resources. To understand the application domain, we analysed the undergraduate course handbook and the unit guides of a computing department. This helped us to identify the domain concepts, the relations between them and the constraints of the domain. Using this domain knowledge, we built an institution specific learning ontology. We show how an ontology-driven approach can be used in an e-learning system to guide the construction of user queries in an unobtrusive way and shield the user from the backend representation. We illustrate how questions can be answered successfully by the e-learning system; and how the learning resources can easily be accessed by using the learning ontology with metadata and user profiles.

A learning ontology with metadata and user profiles for enhancing accessibility of resources

Many OWL ontologies for the learning and education domain have been reported in the literature. The preliminary analysis of those ontologies shows that they use similar constructors and axioms but they do not seem to be following a particular or common fragment (profile) of the OWL language. We analyzed a corpus of 10 representative learning and educational ontologies in order to identify the usage patterns of OWL constructors in those ontologies. Based on our findings and considering the fact that OWL is superseded by OWL 2, we propose OWL 2 Learn, a profile of OWL 2 that is expressive enough for specifying all those ontologies in our corpus with minor modifications. This profile can offer guidance on selecting appropriate constructors for developing an OWL 2 ontology for the learning and education domain.

Sudath R. Heiyanthuduwage

Papers

A Learner Oriented Ontology Of Metadata To Improve Effectiveness Of Learning Management Systems.

An Iterative and Incremental Approach for E-Learning Ontology Engineering

OWL 2 learn profile: an ontology sublanguage for the learning domain

A learning ontology with metadata and user profiles for enhancing accessibility of resources

Towards an OWL 2 Profile for Defining Learning Ontologies