July 2006 Volume 9 Number 3 - CiteSeerX

On a higher layer of abstraction, additional constraints to schemas are introduced through W3C’s Web Ontology
Language (OWL). OWL extends RDF’s OAV schemas using DAML (DARPA Agent Markup Language) and
OIL (Ontology Inference Layer). OWL creates a common vocabulary by adding constraints to class instances,
property value, domain and range of an attribute (property) by providing interoperability functions such as
sameClassAs and differentFrom (Mizoguchi, 2000). However, there are also differences among ontologies.
This paper deals with semantic and structural heterogeneity.
Ontological semantic heterogeneity arises from two scenarios. In the first scenario, ontological concepts for a
domain are described with different terminologies (synonymy). For instance, in Figure 3, the terms booking and
reservation; client and customer are synonymous but termed differently. In the second scenario, different
meanings are assigned to the same word in different contexts (polysemy). On the other hand, different
taxonomies cause structural heterogeneity among ontologies (Euzenat et al., 2004; Noy & Musen, 2000; de-
Diego, 2001; Ramos, 2001; Stumme & Maedche, 2001). Instances of structural differences in Figure 3 are
between the concepts airline and duration.
Figure 3. Semantic differences and structural differences between ontologies
Hence, there is a need for ontology mapping and merging. Ontology mapping involves mapping the structure
and semantics describing learning objects in different repositories whereas ontology merging integrates the
initial taxonomies into a common schematic taxonomy. [As such, in this paper the term merged ontology is used
interchangeably with the term shared ontology].
Problem Statement
Four major issues constrain efforts toward ontology mapping and merging tasks:
1. Semantic differences: Some ontological concepts describe similar domain with different terminology
(synonymy and polysemy). This results in overlapping domains. Thus, there is a need for mapping tools to
interpret metadata descriptions from a lexical and semantic perspective to resolve the problems of synonymy
and polysemy.
2. Structural differences: Structure in ontology mapping and merging refers to the structural taxonomy
associating concepts (objects). Different creators annotate LOs with different ontological concepts. This
creates syntactical differences, necessitating merging tools, which are able to capture the different
taxonomies and merge the taxonomies into a common taxonomy.
3. Scalability in ontology mapping and merging: This is true especially for large ontological repositories where
the growth of LO repositories over the network can be explosive.
4. Lack of prior knowledge: Prior knowledge is needed for ontology mapping and merging using supervised
data mining methods. However, such knowledge is not always available. Thus, unsupervised methods are
needed as an alternative in the absence of prior knowledge.
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