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

Appendix D
Algorithm for Semi-Automatic
Goal Annotation
The semi-automatic goal annotation is implemented through the algorithms in the
following cases.
• A. Match a target activity (av ′ ) of a goal (g) in the goal ontology (G) with the
Activity in a PSAM model(av).
• B. Match a target artifact (a f ′ ) of a goal (g) in the goal ontology (G) with the
Artifact and Output in a PSAM model (a f , o).
– B.I Match a f ′ with the annotation of o.
– B.II Match a f ′ with the annotation of a f which is directly associated with
av through has_Activity relationship.
– B.III Match a f ′ with the annotation of a f which is indirectly associated
with av through has_Output and related_Artifact relationships.
• C. Match a target role (ar ′ ) of a goal (g) in the goal ontology (G) with the
Act-role in a PSAM model (ar).
• D. Match a target constraint (c ′ ) of a goal (g) in the goal ontology (G) with the
Precondition, Postcondition, and Exception in a PSAM model (pre, post,
e).
For the automatic match, we exploit the semantic mapping through both ontology
comparison and string match between ontology references and models. To rank
the mapping results, weights are assigned to the different ways of mapping (σ is for a
weight of ontology comparison. τ is for a weight of string match). In ontology comparison,
three different semantic relationships applied in model annotation are taken
into account in the assignment of weights. The synonym ("same_as") relationship is
given the highest weight for complete match (σ = 1), and the hypernym ("kind_of")
is given a lower weight for a subsumption relationship (σ = 0.8), and the meronym
("phase_of" for activity, "part_of" for artifact, "member_of" for actor-role) is given the
lowest weight for a subset relationship (σ = 0.5). In the string match, the exact match
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