Semantic Annotation for Process Models: - Department of Computer ...

2.3. INFORMATION SYSTEMS AND SEMANTIC WEB 19
Conceptual model vs. ontology
The term "conceptual model" appears contrastively to "logical model" and "physical
model" to differentiate the levels of model abstraction. The enterprise models on the
second row of the Zachman framework are conceptual models. A conceptual model is
often taken as an abstract model and defined as a theoretical construct that represents
phenomena in a certain problem domain, with a set of variables and a set of logical and
quantitative relationships between them. Conceptual model, especially conceptual data
schema – representing the structure perspective, is comparable with ontology because
they share some modeling principles.
A comparison of conceptual data schema and ontology is made in [63]. They are
similar because both consist of concepts, relations and rules 4 . There are two main
disparities discussed in [63]:
1. Conceptual data schema is being preserved in off-time model diagrams; while, ontology
typically is sharable and exchangeable at run-time, i.e. machine-processable
semantics.
2. Unlike conceptual data schema that capture semantics for a given application
domain, ontologies are supposed to capture semantics about real-world domains,
independent from specific application needs, i.e. "relatively" generic knowledge.
Therefore, the generality (application-independency) of knowledge is a fundamental
asset in ontology modeling, and that mostly distinguishes ontology from conceptual
data schema.
The two disparities just characterize the two essential aspects of ontologies as described
in [35]: ontologies define formal semantics for information, consequently allowing information
processing by a computer; ontologies define real world semantics, which makes
it possible to link machine processable content with meaning for humans based on
consensual terminologies.
Conventional conceptual models are still widely used in information systems engineering
and play vital roles in enterprise modeling as we have discussed previously.
Meanwhile, ontologies become key enabling technology for the Semantic Web. Moreover,
ontology modeling can benefit from the existing conceptual modeling methodologies
and tools [5] [19] [38] [104]. Legacy conceptual schema can be also mined and/or
"ontologized" [63]. On the other hand, ontology and the Semantic Web technology are
also found to be applied in enterprise modeling and applications [139] [140].
We are aware of common grounds and differences between the two disciplines in
this work and devote our efforts to build the links between conceptual process models
and process ontologies. The work results in a combination of the usage of ontologies
and conceptual models. Business process models at the conceptual level created for
a given application domain still serve information systems development as Representation
of systems and requirements, Vehicle for communication, Basis of design and
implementation and Documentation and sense-making [76]. Meanwhile the extensive
exchange, integration and reuse of business process models between different organization
can benefit from the Semantic Web technology through annotating models using
ontologies.
4 Rules in conceptual modeling are called "constraints", and they are axioms in ontology.