7.5 Process Knowledge Management System
7.5.3 Simple walkthrough examples
We use some annotation results from the exemplar studies to exemplify how the system architecture and semantic reasoning technique are applied in the application of process knowledge management. Three enterprise models have been annotated with GPO and SCOR ontologies. The annotated model fragments represented in PSAM are stored in the repository, which are to be retrieved, discovered and navigated by model users.
In one example, the model user needs to look for some model fragments about the process which deal with "Bill" in both enterprise systems. The following steps must be taken (formalized in (i), (ii), and (iii)):
1. The search question must be translated as a query using PSAM "language" derived from GPO ontology and terminologies from SCOR ontology.
• The process is defined as Activity and "Bill" is instance of Artifact accord- ing to the GPO ontology.
• The term "Bill" in the user’s question is same as the concept "Bill" in the SCOR ontology.
• The query submitted to the system is then interpreted as "Find the Activ- ities which have Artifact Bill.", which is formalized in (i) with respect to the GPO and SCOR ontology.
2. The system analyzes and executes the query accompanied with reasoner.
• The reasoner employs the T-Box inference to get all the equivalent concepts and all the sub-Classes of Bill in the SCOR ontology, which is formalized in (ii). In this case, Invoice is semantically equal to Bill, and Bill of Materialsis a sub-Class of Bill.
• The query is expanded by the three concepts, i.e. "Find the Activities which have Artifact Bill, Invoice or Bill of Materials".
• According to the PSAM annotation, the model instances of Artifact can have the references of Bill, Invoice, and Bill of Materials through the semantic relationships such as same_as, kind_of and part_of. The query is hereby expanded based on the annotation information, which is formalized in (iii). A-Box inference is applied in (iii) to find out the model fragments (x, y) which are annotated as the instances of Activity and Artifact.
124 CHAPTER 7. EXEMPLAR STUDIES AND APPLICATION SYSTEM Query: ?x|(x, Bill) ∈ R: has_artifact, x ∈ Activity
w.r.t Ogpo,Oscor (i)
T-Box inference: ?C |C = Bill w.r.t. SCOR ontologyOscor
?C |C v Bill w.r.t. Oscor (ii) A-Box inference: ?y|(x, y) ∈ R: has_artifact, y∈ Arti f act w.r.t. Ogpo
(y, C) ∈ R: same_as, kind_of, part_of w.r.t. Ogpo (iii)
After running query and inference tasks, the system finds the following model frag- ments which are about the process dealing with "Bill":
1. PMA: "Check relevant bills". In the original model of PMA, this Activity has an Artifact "bill" which is annotated with the SCOR ontology concept "Bill". 2. PMB2 "Send items". In the original model of PMB2, this Activity has an Arti-
fact "invoice" which is annotated with the SCOR ontology concept "Invoice" (an equivalent concept of "Bill" in the SCOR ontology).
Another example is about a goal query of process knowledge. The model user would like to search the process model fragments having the impact on the goal of improving delivery performance. Similar steps are undertaken formalized in (j), (jj), and (jjj):
1. The search question is translated as a query using PSAM, GPO and SCOR ter- minology.
• Goal is only used to annotate Activity in PSAM, the search target is hence Activity of PSAM in the repository.
• Improve Delivery Performance is a soft goal defined in the SCOR ontol- ogy, which is chosen as the keyword for the query.
• An Activity in PSAM is annotated with a soft goal through the relation- ship positively_satisfies and negatively_satisfies. Therefore, the query submitted to the system is then interpreted as "Find any Activity which positively_satisfies or negatively_satisfies the goal Improve Delivery Performance". The query is formalized in (j).
2. The system analyzes and executes the query accompanied with a reasoner. • A reasoner employs the T-Box inference to get all the equivalent concepts, all
the sub-Classes and all the composition members (has_parts) of Improve Delivery Performance, which is formalized in (jj):
– The sub-Class inference results in Improve Deliver to Customer Performanceand Improve Supplier Delivery Performance.
– Process Invoice Without Error, Ensure Full Delivery and Decrease Percentage of Defective Supplied are the part-goals of Improve Deliver to Customer Performance.
7.5. PROCESS KNOWLEDGE MANAGEMENT SYSTEM 125 – Furthermore, through the transition of the inference, the composi- tion members of Improve Deliver to Customer Performance (i.e. Improve Deliver to Customer On Time Delivery Performance, Improve Deliver to Customer Delivery to Date Performance) and of Improve Supplier Delivery Performance (i.e. Improve Supplier On Time Delivery Performance, Ensure Supplier Delivery to Date Performance) should also be involved in the query. • The query is expanded by those inferred concepts, i.e. "Find the Activities which positively_satisfies or negatively_satisfies the goals Improve Delivery Performance, Process Invoice Without Error, Ensure Full Delivery, Decrease Percentage of Defective Supplied, Improve Deliver to Customer Performance, Improve Supplier Delivery Performance, Improve Deliver to Customer On Time Delivery Performance, Improve Deliver to Customer Delivery to Date Performance, Improve Supplier On Time Delivery Performance, and Ensure Supplier Delivery to Date Performance." • When executing the query formalized in (jjj), C will be replaced by all the
inferred concepts and A-Box inference is applied to locate the instance of Activity (x).
Query: ?x|(x, ImproveDeliveryPer f ormance) ∈ R: positively_satisfies, negatively_satisfies, x ∈ Activity w.r.tOgpo, Oscor (j) T-Box inference: ?C |C= ImproveDeliveryPer f ormance w.r.t. Oscor
?C |C v ImproveDeliveryPer f ormance w.r.t. Oscor
?C |ImproveDeliveryPer f ormance has_parts C w.r.t. Oscor (jj) A-Box inference: (x, C) ∈ R: positively_satisfies, negatively_satisfies
w.r.t. Ogpo (jjj)
After running the query and inference, the system finds the following model frag- ments which might have an impact on the goal of improving delivery performance:
1. PMA: "Check delivery items" (positively_satisfies Improve Delivery Performance); "Correct the delivery quantity" (positively_satisfies Improve Deliver to Customer Performance); "Check availability of delivery items" (positively_satisfies Ensure Full Delivery); "Monitor deliv- ery date" (positively_satisfies Improve Deliver to Customer On Time Delivery Performance and Improve Deliver to Customer Delivery to Date Performance); "Edit partial delivery information" (negatively_satisfies Ensure Full Delivery).
2. PMB1: "Receive items from local suppliers" (positively_satisfies Ensure Supplier Delivery to Date Performance and Improve Supplier On Time Delivery Performance); "Issue the deficit protocol" (negatively_satisfies
126 CHAPTER 7. EXEMPLAR STUDIES AND APPLICATION SYSTEM Improve Supplier On Time Delivery Performance); "Issue an export Invoice" (negatively_satisfies Process Invoice Without Error).
3. PMB2: "Correct orders" (positively_satisfies Ensure Full Delivery); "Is- sue Invoice" (negatively_satisfies Process Invoice Without Error).
7.6
Summary
In this chapter, we have exemplified the semantic annotations— profile annotation, meta-model annotation, model annotation and goal annotation through two exemplar studies. A BPMN model and two EEML models have been selected from two differ- ent business organizations in a same business domain about logistics processing. The representations of two models have shown the diversities of modeling notations, con- ceptualizations, terminologies, and business processing ways. GPO as the semantic mediator for modeling languages, has been mapped to BPMN and EEML in the meta- model annotation phase. Two models have been transformed into the PSAM models with Pro-SEAT. The SCOR reference model provides the process templates and stan- dards of logistics process, and it has been chosen as domain ontology for the model annotation and the goal annotation. The level 3 process elements with inputs and outputs from SCOR have been formalized as SCOR domain ontologies in OWL. In order to facilitate the semantic reference from the PSAM model to the domain on- tology, the SCOR concepts have been organized in Activity, Artifact and Actor-role categories. The goal ontology has been derived from the process elements, the inputs and outputs, and the metrics of performance attributes of the SCOR specifications. The semi-automatic goal annotation has been conducted by Pro-SEAT based on the goal annotation algorithms.
To illustrate the applicability of the semantic annotation approach, we have out- lined a process knowledge management system integrating the semantic annotation components. Making use of the Semantic Web technology, the semantic reasoning such as T-Box and A-Box inference has also been exemplified on the semantic annotation re- sults for the process knowledge management purpose. In next chapters we will conduct more systematic evaluation and applicability validation of our method.
Part III
Evaluation
Chapter 8
Quality Evaluation of the Method
Evaluation of the method and prototype implementation consists of two parts: 1) quality analysis of semantic annotation framework and method, and 2) applicability validation based on annotation results. In this chapter, we focus on the quality of our method. We apply a quality framework — SEQUAL to provide a systematic analysis on the quality of GPO (General Process Ontology), PSAM (Process Semantic Annotation Model) and the annotation tool Pro-SEAT. The quality of the work is evaluated based on the use experience of the exemplar studies, which is also related to the applicability validation of the work in Chapter 9.
8.1
Evaluation Design
Since the underlying theory of our work is information modeling, we look at the criteria and metrics of the evaluation in the information modeling discipline. GPO and PSAM have been created for the knowledge representation of process models. If the PSAM definition in chapters 4 and 5 is regarded as a modeling language, GPO is the meta- model of it, and an instance of PSAM is a model. In our exemplar studies, the original EEML/BPMN process models are translated into the PSAM models in a common pro- cess knowledge modeling language based on GPO. We therefore apply a general quality framework of models and modeling languages in the evaluation. The quality framework is based on the semiotic theory1, which is also the theoretical basis of our annotation framework. Hence the whole work is built and evaluated under the same theoretical foundation. The quality categories are selected from the quality framework and evalu- ated through the exemplar studies. The annotation tool is also evaluated according to the quality framework. However, since it is only a prototype, the performance of the system and the user interface is not taken into account in the evaluation.