=Paper=
{{Paper
|id=Vol-2191/paper17
|storemode=property
|title=Ontology-based Representation of Workflows for Transfer Learning
|pdfUrl=https://ceur-ws.org/Vol-2191/paper17.pdf
|volume=Vol-2191
|authors=Miriam Herold,Mirjam Minor
|dblpUrl=https://dblp.org/rec/conf/lwa/HeroldM18
}}
==Ontology-based Representation of Workflows for Transfer Learning==
https://ceur-ws.org/Vol-2191/paper17.pdf
Ontology-based representation of workflows for transfer
learning
Miriam Herold and Mirjam Minor
Wirtschaftsinformatik, Goethe University, Robert-Mayer-Str.10, Frankfurt am Main, Germany,
{herold, minor}@informatik.uni-frankfurt.de
Abstract. This paper examines the feasibility of using ontologies for transfer
learning in process-oriented contexts. Transfer learning uses the knowledge learned
in a source domain to improve the ability to solve problems in a target domain.
Ontologies can help to store the domain knowledge with all appropriate relations
between the concepts. This work describes an approach for capturing workflows
represented in BPMN language in an ontology. The aim is the transfer of proce-
dural knowledge from a source into a target domain and we study the feasibility
of using a process-oriented ontology as a means for the transfer. We illustrate the
approach with an ontology of workflows from passenger and baggage handling at
the airport. For creating the ontology we transform airport workflows from a pro-
prietary format into BPMN and use the main elements (pool, lane, sequences etc.)
for depicting the workflow structure. Then we represent all existing workflows by
the ontology. The suggested approach is generic and domain-independent. It al-
lows broad opportunities for transfer of procedural knowledge.
Keywords: transfer learning, process-oriented case-based reasoning, ontology
1 Introduction
Management of business processes is a widespread area in the business context. Re-
cently, many enterprises face new challenges such as digital transformation and, thus,
need to adjust their business processes. Digital transformation in the current context
means the transformation of key business operations and affects processes and pro-
duction, as well as management concepts and structure of an organization (Matt et al.,
2015). But there are many other purposes and business areas that require flexibility
and adaptation of existing business processes (Minor et al., 2014a). To overcome these
challenges companies need to adapt the existing workflows according to the changed
conditions. Case-based reasoning (CBR) may provide a support for this purpose as it
is based on the intuition, that similar problems tend to have similar solutions (Richter
and Weber, 2016). After an appropriate adaptation the past solution may help in solving
the current problem. In all settings where workflows are involved, CBR methods can be
extended for process management. Process-oriented case-based reasoning (POCBR)
systems ’are capable to support the creation and adaptation of workflows by reasoning
on cases recording experiential knowledge from previous workflow modelling, execu-
tion, or monitoring activities’ (Minor et al., 2014b). A case in POCBR is typically a
process description or a workflow expressing procedural experiential knowledge (Mi-
nor et al., 2016).
In many domains the knowledge of business processes and workflows is not exces-
sive. Then it is usefull to examine if things learned in one well-known domain can be
adapted and re-used in another related context (Kudenko, 2014). Transfer learning (TL)
addresses this question. In the context of CBR, TL uses knowledge in a source domain
to improve the ability to learn to solve tasks in a target domain, where the knowledge
is sparse (Klenk et al., 2011). This paper is motivated by the idea of transfering the
process-oriented knowledge from a familiar domain to another one lacking in exper-
tise. In the area of business processes there still exists little research on transferability
of cases. We would like to address this research gap and aim to find a novel approach
to achieve the knowledge transfer.
Previous work on the transferability of process-oriented cases (Minor et al., 2016)
has been based on manual ontology construction. In this paper we plan to extend this
idea and examine to find an automated ontology-based transfer learning approach. It is
the first stage of the ongoing project EVER2 1 . We use ontologies as a knowledge base,
as it allows a flexible representation of wide-ranging relations and concepts. Relations
are a very important issue in analogical models, which have been widely examined in
the context of transfer learning. To be able to capture relations of various complexity,
we decided to use ontologies for the representation of procedural knowledge and as a
means for transfer. Ontology is defined as an ’explicit specification of a conceptualiza-
tion’ and includes concepts, relationships, and other distinction relevant for modelling
a domain (Gruber, 2009). In our current work, workflows are annotated by concepts of
an ontology, which contains transfer rules for the knowledge transfer from the source
in the target domain. For the demonstration we used the open-source tool Protégé. The
created ontology represents a case-base for workflows. In the past decade the Business
1 funded by Deutsche Forschungsgemeinschaft (DFG) under the project number MI 1455/2-3
Process Model Notation 2.0 (BPMN) disseminated across enterprises and industries. It
is a standard graphical language for the specification of business processes. Our sug-
gested procedure for transformation via ontology is based on workflows modelled in
BMPN 2.0.
This paper is structured as follows: In the next section we provide an overview of
the related work. In the third section we introduce an approach for ontology-based rep-
resentation of BPMN-workflows. Then we demonstrate the feasibility of our proposed
approach with an example. The paper continues with a draft for the next possible project
direction and future research opportunities. In the last section we summarize our work
and draw some conclusions.
2 Related Work
Transfer learning has been examined in many different contexts, especially in ma-
chine learning (Taylor and Stone, 2009), (Kudenko, 2014) or data mining (Pan and
Yang, 2010). But there is still little research in the area of CBR. CBR systems collect
problem-solution pairs (cases) in a case-base and are able to learn, retrieve, adapt and
use this knowledge for solution of new upcoming problems (Richter and Weber, 2016).
The existing adaptation approaches in CBR can perform a kind of TL-strategy. (Klenk
et al., 2011) describe in their work ’CBR as a transfer learning method’. Our research
is a contribution to process-oriented case-based reasoning and we plan to examine on-
tologies as a means for transfer of procedural knowledge.
In case-based TL there is a considerable amount of research on using analogical
models ((Falkenhainer et al., 1989), (Klenk and Forbus, 2013), (Könik et al., 2009),
(Kuhlmann and Stone, 2007), (Ragni and Strube, 2014)). Analogy represents the over-
lap between the source and the target domain. If the knowledge is stored in a hierarchi-
cal structure the overlap can be located on a higher hierarchical level. The workflows in
a case-base have to be abstracted according specific abstraction rules in the source do-
main and refined in the target domain. Generalization and specification can also be used
as a means for transfer learning (Müller and Bergmann, 2015). (Müller and Bergmann,
2014) propose a compositional adaptation approach using workflow streams. They iden-
tify substitutable components of a workflow and replace them by other suitable work-
flow streams. In our project we plan to extend this research ideas and develop a system
for automated abstraction of process-oriented cases. The aim is to find an appropri-
ate abstraction level, where the overlap is sufficient and allows the knowledge transfer
between two domains.
Process ontology based approach (POBA) as in (Fan et al., 2016) uses ontology pri-
marily to ease semantic ambiguitiy in modelling of business processes. In the first step
of the POBA approach the authors transform an existing non-process domain ontology
in a process ontology. In contrast to our approach, which focuses on transfer learning,
their goal is different. They capture semantic concepts in an unambiquous manner in or-
der to improve the efficiency and quality in modelling of business processes. (Montani
and Leonardi, 2014) developed a framework for supporting of run-time adjustmets and
a subsequent analysis of business processes. Their approach allows retrieval of process
traces (recorded execution order of tasks) similar to the current process. Our focus is
rather on the process models than on the post-mortem analysis of executed workflow
traces.
In the literature there are existing BPMN-ontologies that capture all elements of
BPMN specification (Natschläger, 2011) and (Rospocher et al., 2014). The main focus
in our work is not building an ontology with all possible elements of BPMN 2.0, but
primarily in capturing the existing business processes with the control flow and the
data flow, which are characteristic features of a workflow. The control flow determines
the order of task execution whereas data flow specifies how data items (information
or documents) interact with the tasks. The proposed ontological representation can be
continually extended with new elements based on the increasing workflow repository.
In the past there has been done reasonable effort in the research of semantically
enhanced business process modelling (Abramowicz et al., 2012), (Thomas and Fell-
mann, 2009). The two models describe the conception and creation of process-oriented
ontologies, partly based on BPMN-workflows. The findings are demonstrated on some
examples, but the results as a whole were not publicly available during the initial phase
of our project. To be able to implement our future work we decided to create an own
ontological representation based on the characteristics of workflows from our reposi-
tory.
3 Ontology-based representation of BPMN-workflows
The ontology-based representation of BPMN-workflows introduced in this section
can be used for different domains. Only the creation of taxonomy structures requires
domain expert knowledge. To build the workflow-ontology we follow four steps. Steps
1 and 2 represent terminological knowledge (TBox), while steps 3 and 4 consider as-
sertive knowledge (ABox) (Baader et al., 2004).
Step 1
First we created basic classes for:
- Structural parts as events, gateways, processes (incl. sub-processes) and tasks
- Actors, corresponding to lanes in BPMN
- Documents, which could be consumed or created by tasks
Step 2
In the second step we generated object properties to capture all existing relations
between the elements of a workflow. Object properties can be stored in a taxonomic
structure. We organize them in three classes:
- ActivityPerformingRelations, the main relation in this class is ’do’ to show, for
example, that an actor is responsible for executing a specific task
- AssignmentRelations to express that an element belongs to one specific workflow
or a document is input/output of a particular task
- TemporalRelations to capture the order of tasks, events, gateways etc.
Step 3
Based on the available BPMN-workflows we create instances for all actors, docu-
ments and structural parts.
Step 4
The last step is the representation of all properties of a workflow, using the object
properties and instances created in previous steps:
- assignment of all elements (structural parts, actors and documents) to a specific
workflow
- assignment of all structural parts to a specific actor, according to lanes in BPMN
- assignment of all documents to particular tasks (as input or output)
- setting the order of all tasks or structural parts (e.g. Task1 is executed prior to
Task2)
4 Demonstration of Feasibility
In this early stage of the project the ontology has been edited manually in Protégé.
In future, most parts of the proposed procedure can be automated as all elements of a
BPMN-workflow are transformable from XML to OWL-format according to transfor-
mation rules. Especially the most expensive steps 3 and 4 are highly automatable.
To be able to represent the relations between the workflow elements properly in
Protégé, it is necessary to create them on instance level. Additionally, it is useful to
organize them in a taxonomic structure (class hierarchy), for example similar or syn-
onym tasks can be grouped in one class. This allows to capture and manage additional
(implicit) knowledge. We decided to use OWL as a language because it containes a
richer vocabulary for describing properties and classes compared to RDF. For example
the representation of equivalent or disjoin classes, as well as transitivity of relations
could be useful in our future research. For our tests of feasibility we used eight airport
workflows for passenger and bagagge handling. Fig. 1. demonstrates an example of a
BPMN-workflow.
Fig. 1. Workflow ’Passenger Handling Check-In the Evening Before’
Following the four steps described in the previous section the workflow results in the
ontology showed in Fig. 2. The continuous arrows demonstrate the hierarchy between
the classes and instances. The dashed arrows stand for ActivityPerformingRelations,
TemporalRelations and AssignmentRelations. According to this procedure all existing
airport workflows are captured in the ontology. Based on the object properties it is pos-
sible to query and reassemble the initial workflows from the ontology, including the
appropriate order of tasks. The ontology represents a case-base and serves as a knowl-
edge base for further work.
Table 1. demonstrates some examples for transfering BPMN elements in OWL lan-
guage. The left column depicts the typical BPMN diagrams or their parts and the right
column contains appropriate OWL-code-snippets extracted from the ontology file.
The first line shows the assignment of workflow parts (tasks, events, actors or sub-
processes) to a specific workflow with a particular name. In the workflow example in
Fig. 1 all tasks, events and lanes (corresponding to actors) are part of a workflow with
the name ’Passenger Handling Check-In the Evening Before’. The resulting OWL file
is visualised in the ontology in Fig. 2. The node ’Passenger Handling Check-In the
Evening Before’ in the right bottom corner of Fig. 2 represents the workflow, which is
connected to its elements via dashed lines.
The second line of the Table 1 stands for the assignment of workflow parts (tasks,
gateways, events etc.) to a specific actor. This property captures the relationship ’who
does what’ or ’who is responsible for what’. In our workflow example in Fig. 1 the
Passenger is the actor and thus responsible for the task Enter the Terminal. The third
line shows the relationship between documents and tasks and illustrates if a document
is an input or an output of a task.
Line four demonstrates the sequential order of workflow elements. For example,
it depicts that Task 1 is executed prior to Task 2. In the line five of the table is an
example of instance declaration. According to the step 3 of the procedure described in
Section 3, all elements of a workflow are created as instances. As mentioned before,
these instances can be ordered in a taxonomy.
5 Future Work
In the next project phase, we are going to automate the ontology construction pro-
cedure based on BPMN diagrams. The building of a hierarchical order in the ontology
still requires an intervention of a domain expert. But there is a strong assumption that
the construction of instances and the relations between them can be automated. We are
aware of inaccuracies that can occur in the capturing of workflows, such as order of
tasks (e.g. in one workflow task 1 proceeds prior to task 2 and in another workflow are
those two tasks in a reversed order). Another problem can occur if two tasks are related
in one workflow and in another workflow the same tasks have no relation at all. We
need to find a way to handle these inaccuracies.
In some cases, the knowledge transfer from a source into the target domain is
achievable only on a higher abstraction level. In the next step of our project we are
going to pay attention to an automated abstraction of tasks. One promising approach is
Fig. 2. Ontology of the workflow ’Passenger Handling Check-In the Evening Before’
BPMN 2.0 OWL-Code
1. Assignment of structural parts, actors and documents to a particular workflow
2. Assignment of structural parts to a specific actor
3. Assignment of a document to a specific task
4. Setting of sequential order of tasks or events
5. Creation of instances
Table 1. Examples of BMPN-elements in OWL
the decomposition in meaningful workflow streams and their abstraction. As stated in
(Müller and Bergmann, 2014) the stream candidates must be identified, then stored in
a repository and used for adaptation of workflows. We would like to examine the fea-
sibility of abstracted workflow streams for knowledge transfer between the domains.
(Müller and Bergmann, 2014) define conditions for building of meaningful workflow
partitions. According to their specification, the tasks have to be transitively data-flow
connected and each partition has to end with a creator task (task with a document as
an output). Another condition is the data-flow completeness of a workflow (Müller and
Bergmann, 2016). Our airport workflow examples contain only very few tasks with doc-
ument output, so the conditions as stated in (Müller and Bergmann, 2014) and (Müller
and Bergmann, 2016) can not be fulfilled. For our purpose, we need to define alternative
boundaries for the separation of meaningful workflow streams and their abstraction.
Another topic in our project will be the investigation of a target domain. We plan
to use the knowledge from the source domain (passenger and baggage handling) and
transfer it to another area. The discussion of different potential target domains is still
ongoing. In respect to a short distance transfer, we think about transferring workflow
models between two different airports. A frequent situation in the airport service sector
is the adjustment of processes after a change of the service provider. TL could contribute
to optimize the existing workflows and, thus, support the airport quality management.
Second, cargo processes may be a promising partner domain for passenger and baggage
handling. In addition to transferring entire workflow models, we discuss to learn knowl-
edge on exception handling routines for workflows (Reichert and Weber, 2012, ch. 6)
in one domain and transfer them to the other domain. An example for such an excep-
tion handling in the two airport domains cargo and passenger/baggage handling is the
compensating routine for flight delays, such as providing the customer with a voucher
for beverages in case of a delay message from the electronic status reports of an airline.
Further, potential application fields might be the auditing of airport processes, their
post-mortem analysis, or the recovery of normal operations after a disruption. We also
would like to study transfer learning for more distant domains, e.g. the application of
RFID technology in workflows in different industrial sectors.
6 Conclusions
In our work we proposed a new method for capturing process-oriented domain
knowledge in an ontology. In Protégé, we recorded all parts of a workflow, first on
an instance level and then in a taxonomical hierarchy. The taxonomy shows the rela-
tions between the elements of a workflow and contains further hierarchical knowledge.
Additionally, we captured all relations between the elements of a BPMN workflow as
properties, including the temporal relations. This ensures that any particular workflow
can be restored from the ontology. We desribed the creation of the ontology in a pro-
cedure, which is generic and domain-independent. The ontology can be populated de-
pending on new workflows added to the ontology. We outlined the usage of ontologies
as a means for transfer learning, which of course is a topic of our further research.
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