=Paper=
{{Paper
|id=Vol-1545/om2015_poster8
|storemode=property
|title=Ontology matching techniques for enterprise architecture models
|pdfUrl=https://ceur-ws.org/Vol-1545/om2015_poster8.pdf
|volume=Vol-1545
|dblpUrl=https://dblp.org/rec/conf/semweb/BakhshandehPB15
}}
==Ontology matching techniques for enterprise architecture models==
https://ceur-ws.org/Vol-1545/om2015_poster8.pdf
Ontology Matching Techniques for Enterprise
Architecture Models
Marzieh Bakhshandeh1 , Catia Pesquita2 and José Borbinha2
1
INESC-ID - Information Systems Group, Lisbon, Portugal
2
LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
3
Instituto Superior Técnico, Universidade de Lisboa, Portugal
marzieh.bakhshandeh@ist.utl.pt,
cpesquita@di.fc.ul.pt,jose.borbinha@ist.utl.pt
Abstract. Current Enterprise Architecture (EA) approaches tend to
be generic, based on broad meta-models that cross-cut distinct architec-
tural domains. Integrating these models is necessary to an effective EA
process, in order to support, for example, benchmarking of business pro-
cesses or assessing compliance to structured requirements. However, the
integration of EA models faces challenges stemming from structural and
semantic heterogeneities that could be addressed by ontology matching
techniques. For that, we used AgreementMakerLight, an ontology match-
ing system, to evaluate a set of state of the art matching approaches that
could adequately address some of the heterogeneity issues. We assessed
the matching of EA models based on the ArchiMate and BPMN lan-
guages, which made possible to conclude about not only the potential
but also of the limitations of these techniques to properly explore the
more complex semantics present in these models.
Enterprise Architecture (EA) is a practice to support the analysis, design and
implementation of a business strategy in an organization, considering its rele-
vant multiple domains. In recent years, a variety of Enterprise Architecture [5]
languages have been established to manage the scale and complexity of this do-
main. Integration of EA models is necessary to support EA processes, however
structural and semantic heterogeneities hinder integration. Ontology matching
has been proposed as a useful technique to help address this challenge [4]. On-
tologies and associated techniques are increasingly being recognized as valuable
tools in the EA domain (e.g., [1]).
To evaluate the applicability of ontology matching techniques to address the het-
erogeneity between EA models, we have selected four case studies that demon-
strate heterogeneity challenges at the model level. Cases 1 and 2 showcase Ab-
straction Level Incompatibilities between models encoded in different languages
(ArchiMate and BPMN), that represent similar situations. Cases 3 and 4 illus-
trate both Abstraction Level and Element Description heterogeneties between
models using the same language, where both pairs of models represent the same
situation encoded by different modelers.
To support the matching tasks we have used AgreementMakerLight (AML) [2],
2
an ontology matching system that is extensible and implements several state of
the art ontology matching algorithms. We extended AML to produce subclass
mappings. The generated alignments were manually evaluated.
The four case studies and their matching using a combination of ontology
matching algorithms illustrate the challenges and opportunities in their appli-
cation to addressing EA heterogeneities. As expected, string and word based
techniques are effective at capturing the mappings between equivalent individu-
als who share similar names. However, when equivalent individuals had dissim-
ilar labels, for which WordNet extension did not produce any shared synonyms,
the applied algorithms failed. Regarding Abstraction Level Incompatibilities, the
results were related to the complexity of the models. In simpler model match-
ing tasks, the Subclass Matcher approach had a good performance, identifying
75% of the subclass mappings. However, in more complex tasks performance is
reduced. Since the evaluated approaches relied only on model information to
perform matching, there was no practical difference between matching models
using the same or different languages.
We consider that the main limitation of the employed matching techniques was
their inability to explore a considerable portion of the information modelled in
the ontologies. In order to extend the application of ontology matching tech-
niques to the EA domain, ontology matching systems need to be able to explore
this semantic richness by producing semantic matching approaches that go be-
yond current strategies which are mostly WordNet based [3]. In recent years,
ontology matching systems have had a growing interest in terms of reasoning
capabilities, and we propose that a combination of these strategies with pattern-
based complex matching approaches [6] may provide improved solutions to the
EA model integration challenge.
References
1. Antunes, G., Bakhshandeh, M., Mayer, R., Borbinha, J., Caetano, A.: Using on-
tologies for enterprise architecture integration and analysis. Complex Systems In-
formatics and Modeling Quarterly (1), 1–23 (2014)
2. Faria, D., Pesquita, C., Santos, E., Cruz, I.F., Couto, F.M.: Agreementmakerlight
results for OAEI 2013. In: OM. pp. 101–108 (2013)
3. Giunchiglia, F., Autayeu, A., Pane, J.: S-match: an open source framework for
matching lightweight ontologies (2010)
4. Karagiannis, D., Höfferer, P.: Metamodeling as an integration concept. In: Software
and Data Technologies, pp. 37–50. Springer (2008)
5. Lankhorst, M., et al.: Enterprise Architecture at Work: Modelling, Communication
and Analysis (The Enterprise Engineering Series). Springer (2013)
6. Ritze, D., Meilicke, C., Sváb-Zamazal, O., Stuckenschmidt, H.: A pattern-based on-
tology matching approach for detecting complex correspondences. In: ISWC Work-
shop on Ontology Matching, Chantilly (VA US). pp. 25–36. Citeseer (2009)
1
Acknowledgements:This work was supported by national funds through Fundação
para a Ciência e a Tecnologia (FCT) with references UID/CEC/50021/2013 and
UID/CEC/00408/2013.