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

RLO denotes a remote learning object that is related to LLO.
C denotes a set of concepts representing classes in an ontology.
Λ denotes a classification mapping function. Λ: LLO → C
The classification mapping function can make a classification tag (element) to refer an ontology class and acquire an
additional semantic knowledge about the learning object.
Λ(lo1) = c1 means that the classification of the learning object lo1 is set to class c1.
P denotes a set of properties in an ontology class.
Υ denotes a relation mapping function. Υ: LLO x RLO → P
The relation mapping function can make a relation tag (element) to refer an ontology property and acquire an
additional semantic knowledge about the relationship.
Υ(lo1,ro1) = p1 means that starting learning object of p1 is lo1 and ending learning object of p1 is ro1.
In LOM, this study employs classification and relation metadata elements to provide extra semantic information. The
classification element can indicate a character of the learning object has, while the relation element describes the
meaning of the arc's ending learning object relative to its starting learning object. The content of both classification
and relation elements are URI references that identify the learning object of the intended property. The format of this
learning object is not standardized by LOM, and hence is open for proprietary semantic extensions. In the semantic
LOM framework, each learning object can refer to an additional semantic through a classification element to set a
specific ontology class. Similarly, each relationship between learning objects can refer to additional semantics
through a relation element to set a specific ontology property.
Example 2. An example of instantiated LOM document is shown in Figure 1. Here LLO =cu-1, RLO =cu-4.
Learning object references a specific ontology class through the classification mapping function as, Λ(cu-1) = XML.
In addition, the outbound arc defines a specific ontology property through the relation mapping function as in the
following: Υ(cu-1,cu-4) = XMLParser.
Definition 6. (Rule) A rule RL∈R is a tuple RL = , where
H denotes the head of the rule. H C∪P
B denotes the body of the rule. B C∪P
Exp denotes the rule of the form ‘if b1 and b2 and … and bn then h’, where
h∈H, b1, b2, … , bn ∈B
RLP denotes a set of rule logic programs, such as RuleML, XRML (Lee and Sohn 2003), SRML(Thorpe and Ke
2003) or SWRL (Horrocks, Patel-Schneider et al. 2003 ).
The existing proposals for building a rule layer on top of the ontology layer of the Semantic Web refer to rule
formalisms originating from the field of Logic Programming. The Rule Markup Language (RuleML) provides a
natural mark-up for Datalog rules, using XML tags such as , , , etc.
Example 3. In the example of Figure 1, the instantiated rule head and body can be depicted as h = treeMode ∈P, b1
= XMLParser ∈P and b2= using ∈P.
Reasoning Capabilities of OWL and RuleML
Before formally introducing LOFinder, the reasoning capabilities of OWL and RuleML are discussed. Both are
essential components of LOFinder.
Reasoning Capabilities of OWL
The W3C OWL recommendation comprises three languages, given here in order of increasing expressive power:
OWL Lite, OWL DL (Description Logic) and OWL Full. OWL Lite is a subset of OWL DL, which is in turn a
subset of OWL Full, the most expressive language. OWL Full, extending RDF and RDF Schema to a full ontology
language, provides the same set of constructors as OWL DL, but uses in an unconstrained manner. This study
focuses on OWL DL, and adopts OWL to mean OWL DL without loss of generality. OWL is based on description
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