=Paper=
{{Paper
|id=Vol-1829/iStar17_paper_5
|storemode=property
|title=Supporting i*-Based Context Models Construction through the DHARMA Ontology
|pdfUrl=https://ceur-ws.org/Vol-1829/iStar17_paper_5.pdf
|volume=Vol-1829
|authors=Wilson Pérez,Karina Abad,Juan Pablo Carvallo,Xavier Franch
|dblpUrl=https://dblp.org/rec/conf/istar/PerezACF17
}}
==Supporting i*-Based Context Models Construction through the DHARMA Ontology==
https://ceur-ws.org/Vol-1829/iStar17_paper_5.pdf
Supporting i*-Based Context Models Construction
through the DHARMA Ontology
Wilson Pérez1, Karina Abad1, Juan Pablo Carvallo2, Xavier Franch3
1Universidad de Cuenca (UC), Cuenca, Ecuador
2Universidad del Azuay (UDA), Cuenca, Ecuador
3Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
{karina.abadr, wilson.perez}@ucuenca.edu.ec,
jpcarvallo@uazuay.edu.ec,franch@essi.upc.edu
Abstract. The construction of enterprise context models, fundamental tool to
design of modern information systems, is usually a cumbersome task to lead,
largely due to the gap of communication between the administrative staff and
technical consultants in charge of its construction. In order to make this task
easier previous works encouraging the reuse of i* context elements through the
implementation and use of catalogs has been proposed. In this paper, we make
use of semantic technologies to exploit such catalog, storing its content in a se-
mantic repository. To support this idea, we have created the DHARMA ontolo-
gy following the guidelines proposed by NeOn methodology, integrating differ-
ent domains and their vocabularies.
Keywords: DHARMA Method, ontology network, iStar, iStar catalog.
1 Introduction
Modern Enterprises rely in Information Systems (IS) designed to manage the in-
creasing complexity of the interactions between their operations and context. Enter-
prise Architecture (EA) [1] is a widely accepted approach for architecting IS, starting
from the business strategy to its implementation, allowing the identification of the IS
Architecture. In order to support this process, the DHARMA Method [2] has been
proposed, which allows the discovering of Enterprise IS Architectures starting from
the construction of Context Models (CM) expressed in i* notation.
We have applied this method in many industrial cases, discovering repetitive ele-
ments and therefore a pattern catalog [3], which can be used as template to ease the
construction of CM. Despite of its practical interest, the catalog presents some typical
limitations of syntactic artifacts, including the difficulty to perform queries in natural
language, the identification of synonyms and antonyms, etc.; due these limitations, in
this work we propose the extension of the DHARMA ontology, which integrates dif-
ferent domains and their corresponding vocabularies needed to support all activities of
the DHARMA method. The structure of the resulting semantic repository will im-
prove the search of elements and the construction of CM expressed in i* notation.
This paper is organized as follows. Section 2 presents a background and its related
works, section 3 describes the design of the DHARMA ontology; section 4 shows its
implementation. Section 5 presents some results and validations of the resulting on-
tology and finally, section 6 exposes some conclusions and future works.
2 Background and Related Works
This section summarizes previous concepts required to understand the scope of the
proposal, we briefly describe the NeOn methodology to support the creation of the
ontology network and we also present the DHARMA Method and its activities.
2.1 NeOn Methodology
NeOn Methodology guides the life cycle of an ontology network, which is a collec-
tion of interconnected and interrelated ontologies[4]. It is focused in the reuse of ex-
isting resources within the domain of interest and also supports the dynamic evolution
of the ontology network. NeOn offers i) nine scenarios focused in the reuse of onto-
logical and non-ontological resources, their reengineering and fusion; ii) a glossary of
processes and activities involved in the development of an ontology network; and iii)
methodological guidelines to support various processes and activities. This methodol-
ogy is also supported by a tool (NeOn toolkit), which provides some methods and
software complements to manage the knowledge enclosed by each scenario [4].
2.2 The DHARMA Method
The DHARMA Method (Discovering Hybrid ARchitectures by Modelling Actors)
[2] allows the definition of Systems Architecture (SA) by modelling the organization
and its environment using the i* framework. This method is sustained in i) Porter’s
five market forces [5], designed to reason about potential strategies and to help with
the analysis of the influence of context forces; ii) Porter’s Value Chain, which en-
compasses primary and support activities. The DHARMA Method is structured by
four activities:
Activity 1: Modelling the Enterprise Context. The organization and its strategy
are carefully analyzed, to identify its role inside the context. As result, social depend-
encies are identified and included in the organization CM.
Activity 2: Modelling the Environment of the System. This activity proposes the
introduction of an IS to-be inside the organization and analyzes its impact over the
elements identified in activity 1.
Activity 3: Decomposition of system goals and identification of system actors.
System dependencies in the CM are analyzed and decomposed into a hierarchy of
goals required to satisfy them. The result of this activity is a set of SR diagrams.
Activity 4: Identification of System Architecture. Finally, goals included in pre-
vious SR models are analyzed and systematically grouped into System Actors (SA)
representing atomic domains.
2.3 Related Works
In [6], authors present a meta-model based in ontologies to support the i* frame-
work, called OntoiStar, which integrates models representing the i* model through the
use of ontologies. In [7], authors introduce a methodology for the integration of onto-
logical models of the i* framework and its variants, this methodology lead the authors
to the definition of a new extended ontology, called OntoiStar+.
Based on the need to perform a semantic analysis of the DHARMA Method, au-
thors in [8] developed an ontology network called DHARMA, by extending Onto-
iStar+, adding some vocabularies to include concepts of interest for activities 1 and 2
of the DHARMA Method; in this proposal, we aim to complete that extension, adding
vocabularies to include concepts for activities 3 and 4, and besides, extend OntoiStar+
ontology to include concepts of the iStar 2.0 standard [9]. As result, we will get a
complete ontology network that covers the four activities of the DHARMA method,
including concepts related to iStar 2.0 standard.
3 Design of the DHARMA Ontology Network
This section describes the steps performed to design the DHARMA ontology net-
work, following the guidelines proposed in NeOn methodology. This methodology
proposes 9 scenarios to create an ontology network [10]. Due to the nature of this
project, scenarios 1 (From specification to implementation), 3 (Reusing ontological
resources) and 8 (Restructuring ontological resources) will be implemented.
Scenario 1: From specification to implementation. In this scenario, functional
and non-functional requirements were identified. Functional requirements regarding
to activities 1 and 2 of the DHARMA method were presented in [8] and were identi-
fied through Competency Questions (CQ); Table 1 shows functional requirements for
activities 3 and 4 of the DHARMA method, and new concepts included in iStar 2.0.
Table 1. Excerpt of DHARMA Ontology Requirements Specification
Ontology Requirements Specification – Competence Question Groups
CQG1. Actor Relationship (2 PC)
CQ1. Which are the types of relationship between actors? Partipates_In, Is_A
CQ2. Which are the types of relationship between Participates_In relations? Part_Of, Plays
CQG2. Actor (2 PC)
CQ3. Which are the types of Actors? Agent, Role
CQ4. Which are instances of a type of Actor? Hardware, Software, Human, Organization
CQG3. Intentional Element (IE) (2 PC)
CQ5. Which are IE types? Goal, Task, Resources, Quality
CQ6. Which is the category of an IE? Maintenance, Process, Query, Transaction
CQG4. Intentional Element Relationship (3 PC)
CQ7. How can two IEs be linked? Refinement, NeededBy, Qualification, Contribution
CQ8. Which are the types of relationship between Refinement links? and/or-refinement
CQ9. Which are the types of Contribution links? Make, Help, Hurt, Break
Scenario 3: Reusing Ontological Resources – Methodological guidelines for
ontology reuse. This scenario describes the activities performed in order to reuse
ontological declarations.
Activity 1: Search of ontologies. To cover the requirements defined in scenario 1,
five modular ontologies satisfying the requirements were found: OntoiStar, Onto-
iStar+, Offer-job [11], Classification [11] and ValueChain. These ontologies concep-
tualize knowledge regarding to Organizations, Actors, Dependencies, Usability, Or-
ganizational Areas and Socio-technical relationships.
Activity 2: Evaluation of ontologies declaration. After contrasting the ontologies
mentioned in previous paragraph and the stablished requirements, it can be concluded
that Offer-job and Classification ontologies will satisfy concepts of Organization,
OntoiStar and OntoiStar+ will model concepts of the i* notation, answering questions
related to socio-technical requirements, and ValueChain ontology will be used to
satisfy requirements related to organizational areas.
Activity 3: Selection of ontologies declaration. Offer-job, Classification and
ValueChain ontologies are used entirety in the ontology network, as they satisfy re-
quirements analyzed in previous activity. As mentioned in section 2.3, OntoiStar+ is
an extension of OntoiStar, so, we decided to use OntoiStar+ in our ontology network.
For concepts regarding to the DHARMA method activities and functional require-
ment presented in Table 1 we will perform an enrichment process, which will be pre-
sented in section 4.
Activity 4: Integration of ontologies declaration. Based in the guidelines stablished
in [13], two integration models for the creation of the ontology network will be per-
formed: Reuse of ontologies as they are defined (applicable to OntoiStar+ and Value-
Chain ontologies) and Ontological reengineering (applicable to Offer-job and Classi-
fication ontologies as they include irrelevant definitions for the DHARMA method).
Activity 5: Local inconsistences detection. Offer-job and classification ontologies
include a third ontology called Region to define the language, weather and geograph-
ical region, as this information is irrelevant for the DHARMA network ontology, we
have decided to delete it.
Scenario 8: Restructuring ontological resources. Explained in section 4.
4 Extension of the DHARMA Ontology Network
In this section, we will describe the enrichment process of the DHARMA ontology,
using scenario 8 Restructuring ontological resources based in requirements CQG2,
CQG3 and CQG4 (see Table 1). NeOn Toolkit and Protégé were used to extend the
DHARMA ontology network. Figure 1 shows the resulting network, where Classifi-
cation, Ofer-job, ValueChain and OntoiStar are ontological resources, while
DHARMA and iStar 2.0 represent knowledge from external sources that have been
conceptualized into the ontology network. Text over each link describes the relation-
ship between concepts. As an example, let’s consider the relationship “has organiza-
tion industry” link, which has as source Organization concept (from classification
ontology) and target Industry concept (from Offer-job ontology).
has internaElement has organization industry
O. has organization sector O.
internalElementRelationship
Offer-job Clasification
O. has arganizaton area
iStar 2.0
OntoiStar+
has actor type
has dependency area
O.
DHARMA
ValueChain
Ontology External Source Ad hoc wrapper
Fig. 1. DHARMA Ontology Network
The process to transform concepts into ontological constructors is based in the 5
transformation rules exposed in [6], where, i)each concept, concept relation and enu-
meration class is represented as a class in OWL [12]; ii) each enumeration element is
represented as a class instance in OWL; iii) each class property is represented through
axioms in OWL; iv) each association is represented as an object property in OWL; v)
each enumeration and primitive data are represented as a data property in OWL.
5 Results and Validation
The DHARMA ontology network is composed by 4 ontologies (OntoiStar+, Offer-
job, Classification and ValueChain), additional concepts of the DHARMA method
and iStar 2.0. The resulting ontology has a total of 856 classes, 72 Data Properties,
175 Object Properties and 20 Annotation properties. The URI of the DHARMA on-
tology is http://www.ucuenca.edu.ec/ontologies/DHARMA.owl#.
The ontology was validated by annotating different CM analyzed in [13]. Due to
the complexity of creating a semantic repository, this work presents a brief evalua-
tion. The following example shows an SPARQL [14] query answering the questions
included in CQG2 (see Table 1). Table 2 shows the result for an actor (UC) where
variables type and name are concepts from DHARMA ontology and instance is a
concept from OntoiStar+ ontology (and specified in iStar 2.0).
PREFIX dharma:
PREFIX rdfs:
SELECT ?type, ?instance WHERE {
dharma:Actor/UC a ?instanceC. ?dharma:Actor/UC rdfs:label ?name.
dharma:Actor/UC dharma:has_Actor_TypeActor_source_ref ?typeC.
?instanceC rdfs:label ?instance. ?typeC rdfs:label ?type.}
Table 2. SPARQL Query
Variable Result Ontology
Name UC http://www.ucuenca.edu.ec/ontologies/DHARMA.owl#
Type Organization http://www.ucuenca.edu.ec/ontologies/DHARMA.owl#
Instance Agent http://www.cenidet.edu.mx/OntoiStar.owl
6 Conclusions and Future Work
Ontologies are valuable elements to support the IS modelling process, providing a
knowledge base of the information stored, facilitating its reuse. In this work, we have
presented the development of the DHARMA Ontology Network, which conceptual-
izes the knowledge provided by the DHARMA Method, aiming to define the EA of
an organization, and making use of the i* notation.
Applying NeOn methodology, we have extended an ontology network aiming to
encompass the different domains involved in the construction of CM, by reusing dif-
ferent ontologies and enriching them. Finally, the evaluation and results were present-
ed. As future work, we aim to use reasoners and synonym suggestion modules in
order to infer and generate new IS starting from the knowledge provided by the cata-
log instantiated using the DHARMA ontology. Also, we want to enlarge the ontology
to cover aspects related to structural metrics of the resulting i* context model
[15][16].
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