Vladan Devedzic

Understanding ontological engineering

Ontological engineering has garnered increasing attention over the last few years, as researchers have recognized ontologies are not just for knowledge-based systems---all software needs models of the world, and hence can make use of ontologies at design time [1]. A recent survey of the field [4] suggests developers of practical AI systems may especially benefit from their use. This survey earmarked several application classes that benefit from using ontologies, including natural language processing, intelligent information retrieval (especially from the Internet), virtual organizations, and simulation and modeling.

Model Driven Engineering and Ontology Development

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Ontology Modeling and MDA.

The paper presents Ontology Definition Metamodel (ODM) that enables using Model Driven Architecture (MDA) standards in ontological engineering. Other similar metamodels are based on ontology representation languages, such as RDF(S), DAML+OIL, etc. However, none of these other solutions uses the recent W3C effort – The Web Ontology Language (OWL). In our approach, we firstly define the ODM place in the context of the MDA four-layer architecture and identify the main OWL concepts. Then, we define ODM using Meta-Object Facility (MOF). The relations between similar MOF and OWL concepts are discussed in order to show their differences (e.g. MOF or UML Class and OWL Class). The proposed ODM is a good starting point for defining an OWL-based UML profile that will enable using the well-known UML notation in ontological engineering more extensively.

Converting UML to OWL ontologies

This paper presents automatic generation of the Web Ontology Language (OWL) from an UML model. The solution is based on an MDA-defined architecture for ontology development and the Ontology UML Profile (OUP). A conversion, that we present here, transforms an ontology from its OUP definition (i.e. XML Metadata Interchange -- XMI) into OWL description. Accordingly, we illustrate how an OUP-developed ontology can be shared with ontological engineering tools (i.e. Protégé).

Ontology-Based Automatic Annotation of Learning Content

This paper presents an ontology-based approach to automatic annotation of learning objects’ (LOs) content units that we tested in TANGRAM, an integrated learning environment for the domain of Intelligent Information Systems. The approach does not primarily focus on automatic annotation of entire LOs, as other relevant solutions do. Instead, it provides a solution for automatic metadata generation for LOs’ components (i.e., smaller, potentially reusable, content units). Here we mainly report on the content-mining algorithms and heuristics applied for determining values of certain metadata elements used to annotate content units. Specifically, the focus is on the following elements: title, description, unique identifier, subject (based on a domain ontology), and pedagogical role (based on an ontology of pedagogical roles). Additionally, as TANGRAM is grounded on an LO content structure ontology that drives the process of an LO decomposition into its constituent content units, each thus generated content unit is implicitly semantically annotated with its role/position in the LO’s structure. Employing such semantic annotations, TANGRAM allows assembling content units into new LOs personalized to the users’ goals, preferences, and learning styles. In order to provide the evaluation of the proposed solution, we describe our experiences with automatic annotation of slide presentations, one of the most common LO types.

Using Semantic Web Technologies to Analyze Learning Content

The authors demonstrate how to use semantic Web technologies to improve the state-of-the-art in online learning environments and bridge the gap between students on the one hand, and authors or teachers on the other. The ontological framework presented here helps formalize learning object context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse learning artifacts. On top of this framework, the authors implemented several feedback channels for educators to improve the delivery of future Web-based courses.

Key issues in next-generation Web-based education

This paper analyzes and categorizes limitations and weaknesses of current Web-based educational technology, suggests the steps to overcome them, and presents a framework for developing next-generation Web-based educational systems. It suggests developing Web-based educational applications with more theory- and content-oriented intelligence, more semantic interoperation between two or more educational applications, and realistic technological support to achieve such kinds of flexibility.

A framework for building intelligent manufacturing systems

The paper describes a systematic approach to design and development of software for intelligent manufacturing systems. The approach is based on a multilevel, general object oriented model of intelligent systems. Current methods and software design and development tools for intelligent manufacturing systems either stress particular components of intelligence (e.g., high level domain expertise, or learning capabilities, or fuzziness of decisions), or their domain dependence (e.g., monitoring and control systems, or CAPP systems). It is usually difficult to make extensions of such methods and tools, nor is it easy to reuse their components in developing intelligent manufacturing systems. Considerable efforts are being dedicated to the development of interoperable software components, distributed object environments, and flexible and scalable applications to overcome some of these problems. The approach described in the paper starts with a well founded software engineering principle, making clear distinction between generic, low level intelligent software components, and domain-dependent, high level components of an intelligent manufacturing system. It is extensible and adjustable. It also suggests some steps toward design of future software development tools for intelligent manufacturing systems. Several intelligent systems have been developed using the approach. One of these systems, in the cement manufacturing domain, is briefly overviewed, illustrating how the approach is used in practice. Finally, some informal discussion on the performance and complexity of the approach is presented.

The Pragmatics of Current E-Learning Standards

Experience with building distance learning applications shows that a clear understanding of the big picture of standardization in this area is a necessary prerequisite for successful use of standards in practical developments. This article presents e-learning standards, standardization activities and organizations, standards-based development practices, and driving forces for improving existing standards and developing new ones. With these resources, educators and Web-based education system developers will have the tips necessary to approach, implement, and reuse standards-based distance learning applications

The Tao of Modeling Spaces.

The paper introduces modeling spaces in order to help software practitioner to understand modeling. Usually software engineers often think of a specific kind of models – UML models, but there are many open questions such as: Should we assume that the code we write is a model or not; What are models and metamodels, and why do we need them; What does it mean to transform a model into a programming language. Unlike current research efforts that answer to those questions in rather partial ways, we define a formal encompassing framework (i.e. Modeling spaces) for studying many modeling problems in a more comprehensive way. We illustrate the benefits of that framework for explaining present dilemmas practitioners have regarding models, metamodels, and model transformations.