Improving Business Process Model Quality Using Domain Ontologies
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This paper addresses the issue of improving quality of business process (BP) models by exploiting domain knowledge. Indeed, business process models reflect the business processes of companies. The success of these processes has a direct and undeniable impact on business operations success. Managing them through their underlying models helps improving their effectiveness, consistency, and transparency. BP modeling aims at a better understanding of processes, allowing deciders to achieve strategic goals of the company. However, several studies from the literature showed that in experienced system analysts often produce low-level quality. This situation is partly due to lack of domain knowledge. In this paper, we propose to support this modeling effort with an approach that uses domain knowledge to improve the semantic quality of BP models. We suggest to use ontologies as a mean to capture domain knowledge and meta-modeling techniques to deal with BP models independently of languages in which they are expressed. Our contribution is threefold: (1) the meta-models describing both a domain ontology and a BP model are described, (2) the alignment between the concepts of both meta-models is defined and illustrated, (3) a set of Object Constraint Language mapping rules is provided. A simple case study illustrates the process.
KeywordsDomain knowledge Domain ontology Semantic quality Business process modeling Quality improvement
Nowadays, it is widely recognized that conceptual models play a crucial role in information system’s development. As they are a high level abstraction of the represented reality, they constitute a vehicle for communication, provide a comprehensive documentation, and are the basis for the implementation and the evolution of the developed system as well [14, 31]. However, modeling is the intellectual activity implying stakeholders with different skills, knowledge, and experiences. Several studies [19, 26] show that quality of models produced by novice modelers are less complete and lack flexibility and innovation. This is essentially due to their incomplete domain knowledge and the lacks of expertise in methods used.
Business process (BP) models are conceptual models supposed to provide a complete description of the underlying business processes (BPs). Consequently, companies are today aware of the undeniable impact of a better tuning of business processes on the effectiveness, consistency, and transparency of their business operations. This tuning requires a better understanding and an effective management of BP. However, to achieve the expected benefits it is necessary to rethink the approach of designing these processes. BP modeling is a prerequisite. It is now considered as an engineering activity aiming at providing the actors with a better understanding of the processes in which they are involved. But BP modeling is a difficult task. It needs to be performed by trained experts.
And, what about quality? Quality can be defined as the total of properties and characteristics of a product or service that are relevant for satisfying specific and obvious requirements . The business process modeling approaches share many similarities with conceptual modeling activities, but they are much more complex . Indeed, a business process model captures a dynamic vision of the system through activities descriptions, generally done at a low level of abstraction. The modeler has the difficult task to describe these processes at a more general level of abstraction requiring a good understanding of domain knowledge. This is the reason why the activity of modeling BP requires a high degree of pragmatic expertise generally based on empirical rules and heuristics. The latter are difficult to formalize and to share. Commercial tools for business process modeling activities mainly focus on the accuracy of models based on a set of syntactic criteria imposed by the notation and provide little or no guide to guarantee the quality of produced models.
We propose to assist the modeling activity with a quality centered approach that aims to exploit the domain knowledge. The domain knowledge in Information Systems discipline refers to knowledge provided by both methods and application domain . In our approach we propose to refer to domain ontologies knowledge with alignment rules in order to identify similarities between BP models and domain ontologies elements. The aim is to improve the semantic completeness and expressiveness of BP models according to domain knowledge contained in the ontologies.
This paper is organized as follows. A state of the art is described briefly in Sect. 2. Section 3 introduces a motivating example that will serve to illustrate our solution. The overall approach is broadly described in Sect. 4. The meta-models structuring both BP models and domain ontologies are described in detail in Sect. 5. Section 6 is dedicated to alignment rules. The proposed approach is applied on the motivating example in Sect. 7. Finally Sect. 8 concludes and describes future research.
2 State of the Art
A business process is a set of related activities that transform an input to create an output with added values . Experts in information systems and professionals agree that the success of a company depends particularly on a good understanding of its business processes . Modeling BP is a useful mean to facilitate their comprehensibility. To make a business process model understandable, reliable, and reusable it is important to ensure its quality. Several approaches that work in this direction are described in the literature. We have classified them into three categories: (1) approaches focused on improving BP methods of analysis and design, (2) process quality measurement, and (3) process model quality measurement. In the first category the approaches are intended to provide advice and best practices to improve the quality of models. The hypothesis is that improving the process development impacts positively the quality of available products. As an illustration, we can mention  where the authors propose a set of guides to improve various characteristics of a process model such as clarity, comprehensibility, or accuracy (correctness). Other authors focus on improving the comprehensibility of models by providing naming rules, documentation, and use of icons or symbols graphs . Other approaches, such as , propose a set of best practices encapsulated in reusable and applicable patterns depending on the context.
The second category is concerned with the quality level of business processes and their execution. In this family, we categorize simulation and control of process such as in  where the authors present a set of simulation tools for business process evaluation. Others focus on the verification of certain characteristics, when executing the process. In  for example, the authors present and discuss several techniques for the analysis of processes during execution such as verification, or for the discovery of a process (process mining), etc.
The approach we propose belongs to the third category that addresses the quality from the point of view of its evaluation and improvement. Process model quality has been investigated in different disciplines. Consequently, a variety of standards have been introduced to define, manage, monitor, and improve that quality. In , the authors present a typology and an overall view of the business process model metrics. They mention the five most important measures: coupling, cohesion, complexity, modularity, and finally the size. An approach based on goal–question–metric method (GQM) is proposed in  to help finding, among a set of quality characteristics, those that are relevant in a given framework and to deduce how to measure them. One of the characteristics that has been the subject of several proposals is the complexity [6, 9, 12]. However, these studies are based primarily on structural characteristics of processes and their models. Complexity, as an example of structural characteristics, has a direct impact on comprehensibility and maintainability. Aguilar et al.  adapted the complexity metrics from software engineering to evaluate the complexity of BP models. They conducted a controlled experiment to study the impact of complex process models on their maintainability. The work presented in  aimed at finding out the factors that impact the understandability of process models.
In conclusion, our analysis of the state of the art led us to argue that the quality of BP models is mainly addressed in terms of structural and syntactic aspects and rarely in terms of semantics. In the remainder of this paper, we present our approach, which aims to go a step forward into a semantic quality-based approach of BP models.
3 A Motivating Example
4 An Overview of the Approach for Semantic Quality Improvement
Modeling activity in general and BP modeling in particular are creative activities conducted by modelers using a given notation or modeling language. The result is of course highly dependent on the modeler experience in the notation practice, on his/her interpretation of the reality, and on the decisions he/she makes regarding the choice of concepts and details to be modeled. This explains the fact that several correct but different models can usually be generated from the same reality. However, these models are supposed to be faithful representations of the reality. Thus, the definition of quality requirements for these models is, in fact, a mean to evaluate this modeling activity and ensure a better result. Many factors may be defined to characterize this quality. The semantic quality measures the degree of correspondence between the model and the domain. The semantic quality is related to both completeness and validity of the models; here the BP models . To improve the quality of the models produced, several approaches are possible: assistance in the development process phase by generic methodological guides from experience, measurement and improvement of the specifications quality, reusing approved specifications fragments, etc. In this paper, we propose to exploit domain knowledge, which is supposed to reflect the knowledge shared by a community of actors, in order to improve the quality of process models.
4.1 Identifying Model-Ontology Similarities
In the first step, the approach consists in discovering the mappings between business process model and domain ontology elements. This is supported by the alignment rules that should be generic and independent of both the BP modeling notation and the ontology implementation language. To ensure this characteristics, two meta-models, namely a BP meta-model and an ontology meta-model, have been defined. They are presented in detail in Sect. 5.
Indeed, there exist several notations for modeling BP models, each having its own vocabulary. Writing the rules at the notation level implies specifying the same rules several times to take into account the variety of vocabulary used to point out the same concept. The alignment rules aim to identify similarities between the process model elements and the domain ontology concepts. Once these similarities are identified, they serve as input for both semantic quality evaluation and improvement activities. In this paper, we mainly focus on this alignment activity.
4.2 Evaluating Semantic Quality
Semantic quality expresses the degree of correspondence between the information expressed within a model and the domain that is modeled. It helps evaluating both the validity and the completeness of the model . The validity checks that all the statements of the model are correct and related to the domain. The completeness, as for it, verifies that all the pertinent statements from the domain are captured by the model. In order to evaluate the semantic quality we have identified a set of what we call quality deficiencies such as incompleteness and ambiguity. These deficiencies result from modeling choices producing models that do not cover the intended requirements or with low expressiveness. Such models lead to inadequate systems because of their incompleteness or their misunderstanding by developers during their implementation. Once a similarity has been identified between a BP model element, let it be bpmi and an element from the domain ontology doj, our approach exploits the knowledge from the domain ontology related to doj to detect and measure semantic quality deficiencies according to quality metrics we have defined.
4.3 Quality Improvement
The quality improvement activity consists in suggesting to the analyst or the quality expert a set of improvement guidelines to improve the quality of their models. Again, this step uses the domain knowledge to generate improvement actions. This means that the completeness and even the relevance of these guides rely partly on the quality of the domain ontology. For example, if the approach identified a similarity between bpmi a BP model element—and doj—an element from the domain ontology—and the domain ontology describes a relationship between doj and dok (an other element from the ontology), then our approach will propose an enrichment action on the BP model based on the relationship between doj and dok.
The analyst, with the help of the suggestions made by the approach, adds/removes/modifies elements from the initial model that evolves. However, quality improvement is never done in one shot and this is why the approach is iterative and incremental. The further sections detail the different steps of the proposed approach.
5 Ontology and Process Model Meta-Models
Our approach relies on the assumption that a domain ontology and a BP model share a common knowledge. It relies on alignment techniques to identify the pieces of shared knowledge. To ensure the generality of these rules, we have chosen to define them at a meta-modeling level. Hence, the first contribution is the definition of meta-models representing ontologies and BP models.
5.1 Business Process Meta-Model
There are several advantages of defining such a meta-model. First, the meta-model provides a synthetic representation of concepts used independently of specific notations helping in the understandability of models. Second, as the BP models are not produced by our approach and are the result of an independent modeling process they are not supposed to be expressed in the same notation. The usage of a meta model will allows us to express the several models as instances of the same meta-model enabling the application of our approach on models from several notations. Moreover, this characteristic allows us to define mapping rules for each couple (BP modeling notation, ontology language) in a generic manner (on the meta-model level) and be able to apply them at a specific level for each model. Finally, since we consider that domain knowledge contains also knowledge embedded in methods and consequently in notations, we will use meta-models to integrate completeness, validation, and correctness rules defined by BP notations to enrich our domain knowledge.
The meta-model expresses a business process model as composed of flows of objects and connectors. A flow object can be an event, an activity, or a gateway. An event that occurs impacts the progress of a process. The events could be of three types: initial, intermediate, and final. An activity can be an atomic task if it is not decomposable or a process if it is complex and has a visible structure. A gateway is a mechanism able to manage the convergence or divergence of activities flows. A connecting element can be an association, a sequence, or a message flow. An association is a simple link between two concepts. The sequence flow defines an execution order of activities. A message flow is used to represent exchange of information between two participants in the process. Activities refer to resources. A resource encompasses abstract concepts such as the human agent responsible for execution of the activity and also information produced or consumed by it. The exact role of the resource in the process is explained by the concept of role.
Examples of the meta-model instances are ‘Employee’ and ‘Responsible’ as People Resource concepts. A sequence of the processes ‘Request authorization’ and ‘Analyze request’ led to a process divergence (Gateway). The two processes ‘Establish MO’ and ‘Carry out mission formalities’ may be executed in parallel.
5.2 Ontology Meta-model
The ontology meta-model ensures the independence of domain knowledge representation from the language used for its implementation. There exists in the literature several contributions on ontology meta-modeling. The authors in  introduced simple concepts and constructors (negation, conjunction, disjunction) to define complex concepts. They also defined several relationships including inheritance links, instantiation, and constraints. In  five types of concepts have been proposed to represent the functional requirements (function, object, and environment) and non-functional requirements (constraints, quality).
6 Mapping Process Model and Ontology Meta-Models
Thanks to the precise categorization of PM concepts in both ontology and process model meta-models we are able to predefine some concepts correspondences enabling the mapping of the domain ontology concepts with the PM concepts. We have defined two kinds of mapping, namely type-based mapping and semantics-based mapping.
6.1 Type-Based Mapping Rules
BP model meta-model
BP model connectors
Domain ontology relations
The second type of mapping, presented in the following section, is richer, being based on the semantics of concepts.
6.2 Semantics-Based Mapping Rules
Based on meta-models presented above, we developed a set of matching rules. These rules allow mapping the ontology concepts with elements from process models. They compute similarity distances and are written in Object Constraint Language (OCL) . The similarity computation is based on the names, the synonyms, and keywords associated to ontology concepts. It exploits WordNet and distance computation algorithms from the literature such as Resnik information content , Wu and Palmer path length , and Pathwardhan and Pedersen context vectors .
Syntactic equivalence returns the ontology concepts which are syntactically equivalent (have the same names) to a BP model element.
Synonymy returns a set of ontology concepts that are synonyms of the BP model concept. The synonymy value is calculated by comparing the existence of common names or synonyms based on Wordnet .
More general returns the ontology concepts having a superiority relationship (also called hyperonymy or IS-A relationship) with a concept from the ontology already detected as synonym or syntactically equivalent to a BP model element.
More Specific returns the ontology concepts having an inferiority relationship with a concept from the ontology detected as synonym or syntactically equivalent to the BP model element. These classes of rules are instantiated for each of the concepts of the BP meta-model. For each class, an example of instantiated rule for the PeopleResource BP concept is given below.
6.3 Quality Defects Detection
- Ambiguity results from using different names and constructs to express the same reality. This makes models unclear and generates confusion when trying to understand them. For example, consider a model providing a property ‘telephone number’. If the reality corresponding to this model allows several categories of telephone number such as personal, office, home, and cellular telephone numbers having each its own usage then the property ‘telephone number’ from the model is ambiguous as it represents several realities. For quality evaluation we use the word clarity (as desired characteristics) instead of ambiguity witch is a non-quality property. To compute the clarity of p a PeopleResource element from the BP model one of the metrics is
- Completeness is related to an incomplete representation of the real world. This incompleteness can result from the complexity of concepts for which only a sub-set of the description is captured within the process model or to a lack of domain knowledge of the analysts or both of them. For example, if a BP model considers only missions with train tickets’ cost reimbursement whereas the reality includes also missions with hotel costs then the BP model is incomplete. It is an example of metrics that helps detecting activities (tasks and processes) that could have incomplete input definition.
Abstraction level is related to the use of the suitable level of generality. Indeed, in some cases, using concepts that are general decreases the size of models and thus helps in their understanding. For example, ‘analyse the request’ activity is a general activity that could hide more precise and alternative request treatments. On the contrary, using very specialized terms may decrease the understandability of the models. Indeed, splitting excessively steps such as ‘check the hotel reservation invoice’, ‘check the plan ticket’, ‘check the registration invoice’ activities could be replaced by ‘check the documents’. The relevant choice of an abstraction level depends on several factors among which we can mention the nature of audience (developers or users), the objective of the model (explanation or implementation), etc.
Meaningless states correspond to states and constructs from the models for which no correspondence is found in the ontology. This decreases the relevance of models and has an impact on its intelligibility. In fact, the analyst may add an activity which does not represent a domain-related role, in other words it represents meaningless data.
6.4 Quality Defects Correction
Correcting ambiguity defects consists in replacing a BP model element by a more adequate one. This one is chosen by the analyst from a list of domain ontology synonyms computed by the approach. As we detect ambiguity when we have a concept in the BP model that has more than one corresponding in the ontology, based on the above example, the analyst may replace the telephone number by one of the ontology concepts, such as office or home telephone.
Correcting incompleteness defects In case of incompleteness, the analyst can rely on the knowledge provided by the ontology to complete the missing parts of the model. For example, an activity in the ontology requires abstract/knowledge concepts as a result. The analyst may complete his model by these concepts, for example each activity should have a human resource responsible of its execution. If the approach detects that an activity has no assigned responsible, it is able to provide the user with suggestions based on the domain ontology.
Correcting the abstraction level Likewise, the user can rely on the knowledge provided by the ontology (hypernyms/hyponyms) to choose the adequate abstraction level of the concept improving the model’s comprehensibility.
Correcting the meaningless states occurs when a concept does not match any of the ontology ones. This could mean that the BP model element is out of the scope or that it is not named in a comprehensible way for the domain vocabulary.
7 Application to the Example
To illustrate the alignment activity of our approach, we consider the example of ‘mission order’ process depicted in Fig. 1. An excerpt from the ‘mission plan’ domain ontology is given in Fig. 5. As mentioned above, all the semantic rules are based on type mapping rules, i.e., equivalence takes place between concepts that are not only syntactically equivalent but also type mapped.
7.1 Similarities and Quality Defect Detection
Extract of synonyms’ results
Mission order request
Ask for assignment
Call for a mission
An operation command
Postulate to a work mission
Fill for a mission
Fill for a foreign mission
Fill for a local mission
Provide mission costs details for reimbursement
Supply for reimbursement
Provide the total spent
Provide the compensation paid
Do the transfer
Offer a deal
Extract of hyponyms’ results
Analyze the demand
Examine the demand
Break down the demand
Correct the demand
Review the demand
Carry out mission formalities
Fetch the mission order
Open mission recorder
Provide mission costs details for reimbursement
Provide in France mission costs details
Provide in Europe mission costs details
Support with business expense
Do the transfer
Extract of hypernyms’ results
BP human resources
In addition, concepts that are more general are suggested to each activity/human resource, for example ‘Administrator’ can be replaced by more generic concepts such as ‘Decision maker’ or ‘Executive’. The approach proposes the several alternatives and the analyst has to decide about the most appropriate choice.
Finally, the context provides knowledge allowing the BP model enrichment and/or completion. This context is defined through the keywords and relationships related to domain ontology concepts. The related concepts returned by the function and proposed to the user may help enriching the model. These concepts are ontology concepts related to the BP model concept.
Extract of related concepts
BP model activity
Establish Mission Order
Documents required (abstract)
Time delay (knowledge)
Reserve flight and hotel
Carry out mission formalities
Flight reservation (abstract)
Ticket reservation (abstract)
Calculate reimburse costs
Do the transfer
Bank Account Detail (BAD)
7.2 Improving Detected Quality Defects
Correcting ambiguity defects The ambiguity hampers the possibility to decide whether the statement from the model is meaningful according to the domain. In fact, ‘Mission order request’ activity does not match a domain-specific contract. It is an application form to fill. Thus, the verb ‘fill for’ is more adequate and the mission order is replaced by more precise activities ‘Fill for a foreign mission’ and ‘Fill for a local mission’ activities.
Correcting abstraction level defects provides advices on the generality discourse level to use in order to increase models understandability. For example, ‘analyse request’ activity is a general activity that may skip a lot of detailed activities. Thus, based on the hyponyms provided by the ontology, the analyst can replace it by ‘Examine the demand’, ‘Review the demand’ and ‘Correct the demand’ activities provided by the ontology. In other cases, using very specialized terms may decrease the understandability of the models. For example, the actor ‘administrator’ can be replaced by ‘decision maker’.
Correcting incompleteness defects could be done by enriching the description of existing concepts or by adding new concepts using their context (related concepts).
The quality of business process models is a hot topic for both researchers and practitioners. Many studies showed that the quality of produced models depends highly on the degree of expertise of the modelers. Moreover, modeling activities are practiced by a significant number of non-experts including IT professionals. One of the reasons impacting the quality of produced models is the lack of domain knowledge covering both knowledge about the methods and notations used as well as application domain knowledge.
In this paper, we tried to propose a solution aiming to exploit application domain knowledge for the improvement of BP models. Our approach considers domain ontologies that are produced in several disciplines (web services, health care, administrative processes, etc.) to improve the semantic quality of BP models. We have defined BP model and ontology meta-models in order to build a uniform description of both process models and domain ontologies. We have then defined an alignment process using both type- and semantics-based mappings to detect similarities between concepts from the BP models and domain ontologies. The results serve as an input for the semantic quality evaluation and improvement processes.
The validation of the approach is ongoing. It is being conducted with 20 undergraduate students taking a course on UML activity diagrams. To ensure equal domain knowledge of the participants, the case study deals with diabetes care medical practices unknown by the participants. The validation process aims to verify the impact of domain knowledge on the quality of produced process models. In order to facilitate the experiment, domain knowledge is provided using natural language and structured texts, which is easier to understand by the students than ontologies. However, we need ontologies to manage knowledge easier than unstructured or semi-structured texts for a tool-supported approach.
the participants have to establish, based on a problem statement, an activity diagram aiming at representing a business process model for a type 2 diabetes care.
the models produced by the students are evaluated by a professor, expert in business process modelling and aware of the related domain knowledge, according to quality criteria addressed by the approach (completeness, clarity, etc.).
the participants are provided with domain knowledge described by a clinical pathway related to type 2 diabetes care. They are invited to detect and to correct quality deficiencies of their own models using this domain knowledge.
Finally, the revised models are, once again, evaluated by a professor.
Steps (2) and (3) are conducted in parallel. At the current stage, the first step of the experiment is being completed as we could not start before ending the training on UML activity modelling.
Regarding the alignment rules they need to be completed to cover all the kinds of concepts and relationships semantics.
The approach suffers from some limits. The main one is the fact that the domain knowledge in hand is not necessarily complete. This is the reason why we propose to enrich the sources of domain knowledge with requirements and domain models. Further research will include the enrichment of quality defects detection rules and the development of a metrics suite for semantic quality evaluation. We are also developing a prototype implementing the proposed approach for BP model quality evaluation and improvement. There is also need for more validation effort by conducting real-life case studies with practitioners.
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