=Paper=
{{Paper
|id=Vol-551/paper-25
|storemode=property
|title=An ontology-based data matching framework: use case competency-based HRM
|pdfUrl=https://ceur-ws.org/Vol-551/om2009_poster4.pdf
|volume=Vol-551
|dblpUrl=https://dblp.org/rec/conf/semweb/BaerTL08
}}
==An ontology-based data matching framework: use case competency-based HRM==
https://ceur-ws.org/Vol-551/om2009_poster4.pdf
An Ontology-based Data Matching Framework:
Use Case Competency-based HRM
Peter De Baer1, Yan Tang1, Pieter De Leenheer2
1
VUB STARLab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
2
Collibra nv/sa, Ransbeekstraat 230, 12 Brussels, Belgium
{Peter.De.Baer, Yan.Tang}@vub.ac.be; Pieter@Collibra.com
Abstract. As part of the European PROLIX (Process Oriented Learning and
Information eXchange) project, VUB STARLab designed a generic ontology-
based data matching framework (ODMF). Within the project, the ODMF is
used to calculate the similarity between data elements, e.g. competency,
function, person, task, and qualification, based on competency-information.
Several ontology-based data matching strategies were implemented and
evaluated as part of the ODMF. In this article we describe the ODMF and
discuss the implemented matching strategies.
Keywords: data matching, competency management, matchmaking, ontology
1 ODMF
Semantic data matching plays an important role in many modern ICT systems.
Examples are data mining [6], electronic markets [1], HRM [2], service discovery [5],
etc. Many existing solutions, for example [2], make use of description logics and are
often tightly linked to certain ontology engineering platforms and/or domains of data
matching. This often leads to a knowledge bottleneck because many potential domain
users and domain experts may not be familiar with description logics or the specific
platform at hand. To avoid such potential technical barrier we designed the ODMF so
that it is independent of a particular ontology engineering platform, and does not
require the use of description logics. Instead, we make use of the combination of an
ontologically structured terminological database [3] and a DOGMA ontology [4] to
describe data. Both the DOGMA ontology and the terminological database make use
of natural language to describe meaning. On top of this semantic data model we
developed an interpreter module and a comparison module. Both the interpreter and
the comparator make use of a library of matching algorithms. The matching
algorithms have access to the data model via an API, and may be written in any
programming language that can access this Java API. Via the terminology base, data
can be described and interpreted in different natural languages. We believe that this
multilingualism will improve the usefulness of the framework within an international
setting.
The ODMF is designed to support data matching in general. Currently, the ODMF
has been, however, only implemented and evaluated as part of the European
integrated PROLIX project1. Within the PROLIX platform2, the ODMF supports
semantic matching of competency-based data elements, e.g. competency, function,
person, task, and qualification.
2 Matching strategies
We implemented and evaluated several ontology-based data matching algorithms
within the ODMF. These algorithms relate to three major groups: (1) string matching,
(2) lexical matching, and (3) graph matching. However, most matching algorithms
make use of a combination of these techniques.
1. String matching techniques are useful to identify data objects, e.g. competences
and qualifications, using a (partial) lexical representation of the object. We
selected two matching tools for this type of data matching: (a) regular
expressions and (b) the SecondString3 library.
2. Lexical matching techniques are useful to identify data objects, e.g. competences
and qualifications, using a (partial) lexical representation of the object. In
addition to plain string matching techniques, linguistic information is used to
improve the matching. We selected two techniques to improve the matching: (a)
tokenization and lemmatization and (b) the use of an ontologically structured
terminological database.
3. Graph matching techniques are useful (a) to calculate the similarity between two
given objects and (b) to find related objects for a given object.
References
1. Agarwal, S., Lamparter, S.: SMART - A Semantic Matchmaking Portal for Electronic
Markets. In: Proceedings of the Seventh IEEE International Conference on E-Commerce
Technology, pp. 405 – 408, (2005)
2. Biesalski, E., Breiter, M., Abecker, A.: Towards Integrated, Intelligent Human Resource
Management. In: 1st workshop "FOMI 2005", Formal Ontologies Meet Industry (2005)
3. De Baer, P., Kerremans, K., and Temmerman, R.: Constructing Ontology-underpinned
Terminological Resources. A Categorisation Framework API. Proceedings of the 8th
International Conference on Terminology and Knowledge Engineering, Copenhagen (2008)
4. Jarrar, M., Meersman, R.: Formal Ontology Engineering in the DOGMA Approach. In: On
the Move to Meaningful Internet Systems: CoopIS, DOA, and ODBASE, LNCS, Springer
Verlag, pp. 1238-1254 (2002)
5. Shu, G., Rana, O. F., Avis, N. J., Dingfang, C.: Ontology-based semantic matchmaking
approach. In: Advances in Engineering Software 38, pp. 59–67, ScienceDirect (2007)
6. Stamou, S., Ntoulas, A., and Christodoulakis, D.: TODE- an ontology based model for the
dynamic population of web directories. In: “Data Mining with Ontologies: Implementations,
Findings and Frameworks”, edited by Nigro, H., O., Cisaro, S., E., G., Xodo, D., H., IGI
Global (2007)
1
http://www.prolixproject.org/
2
http://prolixportal.prolix-dev.de/
3
http://secondstring.sourceforge.net/