Examples
Here we give two ontologies that are presented in the book and the AIR tool. The first ontology is a Petri net ontology that formalizes the representation of Petri nets. The second ontology covers issues in the e-learning domain. Both ontologies are developed in the GOOD OLD AI Ontology UML Profile, because the XSLT converter can transform such models into an OWL representation. Such OWL ontologies can be imported into the Protégé ontology editor and further edited. Besides, we give a MagicDraw project containing a definition of the Ontology UML Profile here.
Petri Net OntologyEducational Ontology
AIR
Petri Net Ontology
Petri nets are formal tool for the modeling, simulation, and analysis of various kinds of systems. These may be distributed systems, communication protocols, multiprocessor systems, Web services, agent systems, object-oriented systems, or adaptive hypermedia systems, to name but a few of the present uses of Petri net. A Petri net graph consists of two types of nodes: places and transitions.
Having in mind the extensible nature of Petri nets and of many Petri net dialects, the Petri net ontology has been organized to have a common part that contains concepts common to all Petri net dialects. This common part may be specialized to a concrete Petri net dialect. The common part of the Petri net ontology we call the core Petri net ontology. Here we give you two versions of the Petri net ontology developed by using the GOOD OLD AI Ontology UML Profile and Poseidon for UML:
Both ontologies can be converted into OWL by using the XSTL converter. You can find more details about the Petri net ontology and related tools at http://www.sfu.ca/~dgasevic/projects/PNO/.
Educational Ontology
Specifying reusable chunks of learning content and defining an abstract way of describing designs for various units of learning (e.g., courses and lessons) are two of the most current research issues in the e-learning com-munity. One field of research in this area is that of learning objects. A learning object is any entity, digital or nondigital, that can be used, reused, or referenced during technology-supported learning. The second field of research referred to as learning design, which can be defined as the application of a pedagogical model to a specific learning objective, a specific target group, and a specific context or knowledge domain. We have developed a set of ontologies to link learning designs and learning content. To do so, we have identified a need for the following three ontologies: (a) an ontology of learning object content, (b) an ontology of learning design, and (c) an ontology connecting those two ontologies.
All these ontologies are developed by using the GOOD OLD AI Ontology UML Profile and Poseidon and they can are available here. They can be converted into OWL by using the XSTL converter.
Learning Object Content Ontology
The Abstract Learning Object Content Model (ALOCoM), the result of a recent EU ProLearn NoE project, is used as a basis for an ontology that describes learning object content. The ALOCoM was designed to generalize all of the other content models mentioned, to provide an ontology-based platform for integrating various content models, and to enable (semi-)automatic reuse of components of learning objects by explicitly defining their structure. On top of that model, we built an ontology called the ALOCoM ontology . This ontology is divided into two parts:
- the ALOCoM Content Structure (CS) ontology, which enables a formal representation of learning objects decomposed into components;
- the ALOCoM Content Type (CT) ontology, which defines the educational role of learning objects and their components.
These two ontologies are used in the TANGRAM project.
LOCO - An Ontology Compatible with IMS-LD
We have used the IMS Learning Design (LD) Information Model as a blueprint for the crea-tion of an IMS-LD-based ontology named the Learning Object Context Ontology (LOCO).
LOCO-Cite - An Ontology for Bridging the Learning Object Content and Learning Design Ontologies
The final step is to create an ontology that serves as a bridge linking the LOCO and the ALOCoM ontology in accordance with the conceptual model of learning object contexts. Because this makes an explicit reference to a specific learning object, we have named this ontology LOCO-Cite. The LOCO and the ALOCoM ontology must be related to each other through LOCO-Cite, which links properties and classes across the boundaries of the individual ontologies to create a larger, unified ontology.
AIR
AIR is an integrated AI development environment based on MDA modeling concepts. Using the MDA philosophy in AIR makes it possible to employ mainstream software technologies that users are familiar with, and expand these technologies with new functionalities. AIR is the first implementation of the Ontology Defintion Metamodel. You can download AIR here.