January 2012 Volume 15 Number 1 - Educational Technology ...

logic (Baader, Calvanese et al. 2003), which is a subset of first-order logic that provides sound and decidable
reasoning support. The OWL allows the terminological hierarchy to be specified by a restricted set of first-order
formulae. Additionally, OWL supports the development of an ontology multiple-layered architecture (Hsu and Kao
2005) that effectively integrates domain level ontologies with top level ontologies. These ontologies are a popular
research topic in various communities. They provide a shared and common understanding of a domain that can be
communicated between people and across application systems (Studer, Benjamins et al. 1998).
XML-based RuleML Logic Program
This knowledge is most appropriate represented using implication. With implication, the author can specify that Y
(the consequence) is satisfied whenever X1...Xn (the antecedents) are all true. One of the simplest approaches for
representing such implication is through rules expressions, which could be Horn clause statements. Unfortunately,
general Horn clause statements are not explicitly representable using the primitives in OWL. OWL can represent
simple implication as described in the previous section, but it has no mechanism for defining arbitrary, multi-element
antecedents. For example, OWL’s description logics can not represent the following non-monotonic rule.
if XMLParser(XML,JAXP) and using(JAXP,DOM)
then treeMode(XML,DOM)
Several researchers have shown how to interoperate, semantically and inferentially, between the leading semantic
Web approaches using rules (for instance, RuleML logic programs) and ontologies (for instance, OWL description
logics) by analyzing their expressive intersections (Grosof and Poon 2003).
System Architecture of LOFinder
LOFinder can be associated with various domain knowledge and metadata. The basic function of LOM is to provide
metadata for e-learning applications. LOFinder supports three different approaches for finding dynamic correlation
of learning object, namely LOM-based metadata, ontology-based reasoning and rule-based inference.
The first approach, called LOM-based metadata, adopts XML-based LOM metadata to describe learning objects.
This approach is used to develop existing e-learning applications, but still cannot intelligently locate relevant
learning objects. The ontology-based reasoning approach provides OWL-based ontologies based on description
logics to provide sound and decidable reasoning to LOM, and therefore can enhance the semantic reasoning
capabilities of LOM. The rule-based inference approach can support inference capabilities that are complementary to
those of an ontology-based reasoning approach. By building rules on top of ontologies, this approach can add
intelligence based on logic programs.
The core components of LOM shell, LOFinder, include the LOM Base, Knowledge Base, Search Agent and
Inference Agent. The flow-oriented LOFinder architecture is depicted in Figure 2.
˙LOM Base: is an annotation repository composed of LOM-based metadata documents, which plays the same
role as the fact base in a traditional expert system. A LOM-based metadata document is an XML document
containing a set of relation metadata and classification metadata. The LOM Base is located on XML layer of the
multi-layered semantic framework.
˙Knowledge Base: is developed based on the Semantic Web technologies to support reasoning tasks, and plays
the same role as the knowledge base in a traditional expert system. It is grouped into two categories: Ontology
Base and Rule Base. The former corresponds to the ontology layer of the multi-layered semantic framework,
while the latter corresponds to the rule layer. The Ontology Base comprises OWL-based ontologies for semantic
reasoning. The Rule Base comprises RuleML-based rules to support a flexible and complex reasoning
mechanism, which cannot easily be achieved by OWL-based ontologies.
˙Search Agent: is a search engine that supports for a XPath query on an LOM-based metadata document
collection within the LOM Base.
˙Inference Agent: is an intelligent agent implemented based on a JESS-based rule engine (JESS 2007). It
converts semantic of OWL-based ontologies and RuleML-based rules to JESS-based rules before starting an
inference.
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