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

1.4. APPROACH AND SCOPE 7
1.4.1 Semantic reconciliation of business process models
The central topic of the research is within information system modeling area. Thus, the
working basis is modeling theory. We distinguish semantic heterogeneity into two levels:
meta-model and model, which correspond to the M2 and M1 layers defined in MOF [124].
Although there is semantic heterogeneity on the M0 layer (instance level models), we
do not take instance level models as reusable knowledge in this research. Therefore,
ontologies related to meta-model and model levels are required for the annotation in
this approach. For meta-model level, an ontology should supply common terminology
and conceptualization of process modeling. There is no mature process ontology as we
know, though there are many ongoing activities relevant to this topic [12] [139] [140].
Based on the investigation and analysis of numbers of existing process ontologies and
process meta-models, we propose a process ontology which consists of most essential
concepts for process modeling languages. For model level, an ontology should contain
standardized terms and definitions of concepts and relationships about a certain
domain. Domain-specific ontologies should be created by domain experts. Such ontologies
can be built based on certain domain standards or reference models. Besides,
to facilitate process knowledge management, a set of profile metadata is required to
describe process models as products. Furthermore, process models can be pragmatically
discovered based on intentional knowledge by goals of process. To explicate such
knowledge, intentional concepts are represented in a goal ontology. Goal concepts are
used to annotate intentional usage of process models. In our approach, all those ontology
references and profile information are annotated to the original models through
a set of metadata. Metadata can aid in the identification, discovery, assessment, and
management of the described information-bearing entities [28]. In this way, annotation
does not intervene the original semantic representation and it is stored separately from
the original models. One original model can have several versions of annotation serving
different purposes. As an outcome of the research work, a semantic annotation framework
is developed to systematically organize the above four perspectives, i.e. profile
annotation, meta-model annotation, model annotation and goal annotation.
Annotating models with ontologies is a procedure to establish certain mapping
between objects in models and in ontologies. Two objects from the different sets —
’annotater’ and ’annotatee’ have not only a simply one-to-one correspondence but more
comprehensive semantic relationships. We define a set of mapping strategies and rules
to guide users to conduct the annotation. An annotation model — PSAM (Process
Semantic Annotation Model) is formalized following the proposed framework and the
mapping method. The PSAM model provides a basis for the implementation. An
annotation system is proposed and implemented as a prototype in order to prove the
feasibility of the proposal.
1.4.2 Machine-interpretable process knowledge
From a technical aspect, semantics of ontologies and PSAM models should be machineinterpretable.
Emerging Semantic Web technology can encode semantics of Web resources
in a machine-readable form. The OWL Web Ontology Language is a language
for defining and instantiating Web ontologies. An OWL ontology may include descriptions
of classes, properties and their instances. Given such an ontology, the OWL formal