RuleML‐based learning object interoperability on the Semantic Web

Abstract

Purpose

The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions.

Design/methodology/approach

As proof of concept, a rule set is implemented using the rule markup language (RuleML), a member of XML‐based languages. This representation in RuleML allows the rule base to be platform‐independent, flexibly extensible and executable.

Findings

The RuleML source representation is easily converted to other XML‐based languages (such as RDF, OWL and XMI) as well as incorporated into, and extracted from, existing XML‐based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT).

Practical implications

The RuleML facts and rules represented in the positional slotted language are used by the OO jDREW reasoning engine to detect and map between semantically equivalent components of course outlines as the key step in their interoperation. In particular, this will enable the precise delivery of learning objects (e.g. course outlines) from repositories to a specific learner's context.

Originality/value

Although the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.

Citation

Biletskiy, Y., Boley, H., Ranganathan, G.R. and Boley, H. (2008), "RuleML‐based learning object interoperability on the Semantic Web", Interactive Technology and Smart Education, Vol. 5 No. 1, pp. 39-58. https://doi.org/10.1108/17415650810871574