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

136 CHAPTER 8. QUALITY EVALUATION OF THE METHOD
concepts. For example, the meronym is represented by "phase_of" for Activity,
"part_of" for Artifact and "member_of" forActor-role.
5. Tool appropriateness. PSAM is defined using a formal syntax and modeled in
OWL. OWL is XML-syntactic compilable and it can be parsed by available commercial
or non-profit parsers. The semantics of GPO and PSAM are also formally
defined according to the OWL semantic definitions. The GPO concepts are modeled
as owl:Class and the relations are modeled as owl:ObjectProperty and
owl:DatatypeProperty. For our work, OWL DL is the basis of the annotation
models. OWL DL was designed to support the existing Description Logic business
segment and has desirable computational properties for reasoning systems [195].
The Protégé-OWL API is integrated in the annotation tool so that the syntax
and the semantics of GPO and PSAM can be interpreted by the tool.
6. Organizational appropriateness. Due to the fair comprehensibility appropriateness
and the application goals of this work, GPO and PSAM are theoretically
easy to understand for audiences from different enterprise units. However, further
empirical evidence is required to establish this claim.
8.3.2 Quality analysis of the annotation model instances
A specific annotation model is an instance of the PSAM model that is transformed
from the original process models and annotated with ontological concepts. The quality
of GPO and PSAM will impact the quality of the PSAM models. The evaluation
of the PSAM models concludes how the model quality relates to the usability of the
annotation results.
Physical Quality
We first look at how the knowledge of the domain has been externalized by the annotation
models. Such a goal could be achieved through the means of domain appropriateness,
participant language knowledge appropriateness and knowledge externalizability
appropriateness. As we have discussed in the quality analysis of those appropriateness
above, the PSAM models can present most information about the process and
functional perspective. The EEML and BPMN models have presented the logistics
processing comprehensively due to their expressiveness. Based on the meta-model annotation
results, the original models are transformed into the PSAM models. Ideally,
the transformation should keep exactly the same information as the original models. It
is obvious that more one-to-one mappings in the meta-model annotation, more knowledge
represented in the modeling elements can be preserved after the transformation.
In the exemplar studies, five one-to-one mappings are in the annotation of BPMN and
three are in the annotation of EEML. In EEML, different resources are specified and
they all could be mapped to the GPO concepts Artifact and Actor-role respectively.
Thus, the PSAM models of PM A should have better physical quality than the ones of
PM B . Fortunately, those specific resources are not much applied in the original EEML
models PM B . That is, not much domain knowledge is lost because of the meta-model