align++ - A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment
Alexandra Mazak, Bernhard Schandl, Monika Lanzenberger
2010
Abstract
Frequently, ontologies based on the same domain are similar but also have many differences, which are known as heterogeneity. The alignment of entities which are not meant to be used in the same context, or which follow different modeling conventions, may cause mismatch in ontology alignment. End-users would benefit from knowing the risk level of mismatch between ontologies prior to starting a time- and cost-intensive procedure. With our heuristic-based method align++ we propose to consider the general application context of a modeled domain (the modeling context) in order to enhance the user support in schema-based alignment. In the method’s first part, ontology concepts are enriched with weighting meta-information, resulting from two indicators: importance weighting indicator and importance outdegree indicator. These indicators contain model- and graph-based information and can be observed and measured at the schema level of an ontology. The output of the first part are ranking lists of importance indicators for each ontology concept in the role of a domain class. In the second part, the candidate sample for our mismatch-risk model bases on external user input by manually identifying concepts between the lists of each source ontology. The heterogeneity risk among the concepts’ importance indicator values is measured as standard deviation over the candidate sample. Afterwards these measured values are aggregated, and a heterogeneity coefficient is calculated. On the basis of this risk factor the mismatch-at-risk (MaR) between ontologies can be approximated as a threshold for schema-based ontology alignment.
References
- Benerecetti, M., Bouquet, P., and Ghidini, C. (2001). On the dimensions of context dependence. In Third International and Interdisciplinary Conference, CONTEXT, Dundee (UK).
- Bouquet, P., Euzenat, J., Franconi, E., Serafini, L., Stamou, G., and Tessaris, S. (2004). D2.2.1: Specification of a common framework for characterizing alignment. Knowledge Web Consortium.
- Chalupsky, H. (2000). OntoMorph: A translation system for symbolic logic. In Anthony G. Cohn, F. G. and Selman, B., editors, KR2000: Principles of Knowledge Representation and Reasoning, pages 471-182, San Francisco, CA.
- Dean, M. and Schreiber, G. (2004). OWL Web Ontology Language Reference (W3C Recommendation 10 February 2004). World Wide Web Consortium.
- Ehrig, M. (2007). Ontology Alignment: Bridging the Semantic Gap, volume 4 of Semantic Web And Beyond Computing for Human Experience. Springer, 1st edition.
- Ehrig, M., Haase, P., Hefke, M., and Stojanovic, N. (2004). Similarity for Ontologies - a Comprehensive Framework. In In Workshop Enterprise Modelling and Ontology: Ingredients for Interoperability, at PAKM 2004, Vienna (Austria).
- (2002). Bankenbezogene Risiko- und Erfolgsrechnung. Schäffer-Poeschel Verlag, Stuttgart (DE).
- Euzenat, J. (2001). Towards a Principled Approach to Semantic Interoperability. In Workshop on Ontologies and Information Sharing, IJCAI01, Seattle (WA US). http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.13.9779.
- Euzenat, J. and Shvaiko, P. (2007). Ontology Matching. Springer, Heidelberg (DE).
- Euzenat, J. and Valtchev, P. (2004). Similarity-based ontology alignment in OWL-Lite. In The 16th European Conference on Artificial Intelligence, ECAI-04, Valencia (Spain).
- Franke, J., Härdle, W., and Hafner, C. (2004). Einführung in die Statistik der Finanzmärkte, volume 2 of Statistik und ihre Anwendungen. Springer, 1st edition.
- Giunchiglia, F. and Shvaiko, P. (2003). SEMANTIC MATCHING. Technical Report DIT-03- 013, University of Trento Department of Information and Communication Thechnology, 38050 Povo, Trento (IT), Via Sommarive 14. http://eprints.biblio.unitn.it/archive/00000381/01/013. pdf.
- Gronback, R. C. (2009). Eclipse Modeling Project: A Domain-specific Language Toolkit. Addison-Wesley, 1st edition.
- Grü ninger, M. and Fox, M. S. (1995). Methodology for the Design and Evaluation of Ontologies. In International Joint Conference on Artificial Inteligence IJCAI95, Workshop on Basic Ontological Issues in Knowledge Sharing, Toronto (CA).
- Horridge, M. (2004). A Practical Guide To Building OWL Ontologies With The Protege-OWL Plugin. University of Manchester, 1 edition. http://owl.cs.manchester.ac.uk/tutorials/protegeowltu torial/.
- Janiesch, C. (2010). Situation vs. Context: Considerations on the Level of Detail in Modelling Method Adaptation. In 43rd Hawaii International Conference on System Sciences, pages 1-10. IEEE Computer Society.
- Klein, M. (2001). Combining and Relating Ontologies: An Analysis of Problems and Solutions. In Gomez-Perez, A., Gruninger, M., Stuckenschmidt, H., and Uschold, M., editors, Workshop on Ontologies and Information Sharing, IJCAI'01, Seattle (WA).
- Mazak, A., Schandl, B., and Lanzenberger, M. (2010). Enhancing Structure-based Ontology Alignment by Enriching Models with Importance Weightings. In 3rd International Workshop on Ontology Alignment and Visualization (OnAV'10), Krakow (Poland).
- Meintrup, D. and Schäffler, S. (2005). Stochastik. Statistik und ihre Anwendungen. Springer, 1st edition.
- Noy, N. F. and McGuinness, D. L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Technical Report SMI-2001-0880, Stanford University, Stanford (CA), 94305.
- Noy, N. F. and Musen, M. A. (2001). Anchor-PROMPT: Using Non-local Context for Semantic Matching. In Workshop on Ontologies and Information Sharing at the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), Seattle (WA).
- OAEI (2009). OAEI-2009 Campaign Conference track. http://oaei.ontologymatching.org/2009/conference/.
- Rahm, E., Do, H.-H., and Maßmann, S. (2004). Matching Large XML Schemas. In SIGMOD Record, volume 33. ACM.
- Shvaiko, P. and Euzenat, J. (2004). A Survey of Schemabased Matching Approaches. Technical Report DIT04-087, University of Trento, Department of Information and Communication Technology.
- Stahel, W. A. (2000). Statistische Datenanalyse. Vieweg & Sohn Verlagsgesellschaft mbH, 3rd edition.
- Visser, P. R. S., Jones, D. M., Bench-Capon, T., and Shave, M. (1997). An analysis of Ontology Mismatches; Heterogeneity versus Interoperability. In AAAI 1997, Spring Symposium on Ontological Engineering, Stanford (CA US). http://citeseerx.ist.psu.edu/viewdoc/summary?doi= 10.1.1.26.6709.
- Wu, G., Li, J., Feng, L., and Wang, K. (2008). Identifying Potentially Important Concepts and Relations in an Ontology. In Proceedings of the 7th International Conference on The Semantic Web, Karlsruhe (Germany).
Paper Citation
in Harvard Style
Mazak A., Schandl B. and Lanzenberger M. (2010). align++ - A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010) ISBN 978-989-8425-29-4, pages 17-26. DOI: 10.5220/0003063600170026
in Bibtex Style
@conference{keod10,
author={Alexandra Mazak and Bernhard Schandl and Monika Lanzenberger},
title={align++
- A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)},
year={2010},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003063600170026},
isbn={978-989-8425-29-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)
TI - align++
- A Heuristic-based Method for Approximating the Mismatch-at-Risk in Schema-based Ontology Alignment
SN - 978-989-8425-29-4
AU - Mazak A.
AU - Schandl B.
AU - Lanzenberger M.
PY - 2010
SP - 17
EP - 26
DO - 10.5220/0003063600170026