Ontology Mapping and Rule-Based Inference for Learning Resource Integration

Abstract

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

A Model-Driven Approach for Reconfigurable Systems Development

“…Secondly, for each AADL error, ontology offers the needed concepts to represent error models in terms of states and events, as hardware errors, computational problems and memory exceptions, and thirdly, the ontology offers the connections and subcomponents (constructs) for component implementation. To achieve this task, we use ontology-based representation of component to detect rules custom inference [39]. The system detects three inconsistencies types making certain formal analysis models invalid: error of incomplete or missing transition; conflict transition that may occur when more than one event is triggered simultaneously; and finally, error in the state of a component due to an error in the failure scheduling scenario.…”