User Guide






Ontology Alignment


Ontologies are seen as the solution to data heterogeneity on the web. However, the available ontologies could themselves introduce heterogeneity: given two ontologies, the same entity can be given different names or simply be defined in different ways, whereas both ontologies may express the same knowledge but in different languages. 

Semantic interoperability can be grounded in ontology reconciliation. The underlying problem, which we call the "ontology alignment'' problem, can be described as follows: given two ontologies each describing a set of discrete entities (which can be classes, properties, predicates, etc.), find the relationships (e.g., equivalence or subsumption) that hold between these entities.

Alignment results can further support visualization of correspondences, transformation of one source into another or formulation of bridge axioms between the ontologies.

The alignment problem can be approached from various standpoints and this fact is reflected in the variety of alignment methods that have been proposed in the literature. Many of them are rooted in the classical problem of schema matching in the database area [4,7] while others have been specifically designed to work with ontologies [6,1]. Some methods rely on formal reasoning about the structure of the entity descriptions [3], others use a combination of similarity-based and graph-based reasoning [5] while a third, mainstream, group apply data analysis [8,9] or machine learning techniques [2] to make emerge good alignments. Our own system, OLA [12], relies on the classical similarity-based paradigm for entity comparison. The exact similarity measure used by the system [11] was derived from the one proposed in [10].


[1] Rose Dieng and Stefan Hug. Comparison of "personal ontologies" represented through conceptual graphs. In Proc. 13th ECAI, Brighton (UK), pages 341–345, 1998.

[2] An-Hai Doan, Jayant Madhavan, Pedro Domingos, and Alon Halevy. Ontology matching: A machine learning approach. In Steffen Staab and Rudi Studer, editors, Handbook on Ontologies in Information Systems, pages 397–416. Springer-Verlag, Heildelberg (DE), 2003.

[3] Fausto Giunchiglia and Pavel Shvaiko. Semantic matching. In Proc. IJCAI 2003 Workshop on ontologies and distributed systems, pages 139– 146, 2003.

[4] Jayant Madhavan, Philip Bernstein, and Erhard Rahm. Generic schema matching using Cupid. In Proc. 27th VLDB, Roma (IT), pages 48–58, 2001. http://research.microsoft.com/ philbe/CupidVLDB0

[5] Sergey Melnik, Hector Garcia-Molina, and Erhard Rahm. Similarity flooding: a versatile graph matching algorithm. In Proc. 18th International Conference on Data Engineering (ICDE), San Jose (CA US), 2002.

[6] Natalya Noy and Mark Musen. Anchor-PROMPT: Using non-local context for semantic matching. In Proc. IJCAI 2001 Workshop on ontology and information sharing, Seattle (WA US), pages 63–70, 2001. http://sunsite.informatik.rwth-aachen.de/Publications/CEURWS/Vol-47/

[7] Erhard Rahm and Philip Bernstein. A survey of approaches to automatic schema matching. VLDB Journal, 10(4):334–350, 2001.

[8] Steffen Staab and Alexander Mädche. Measuring similarity between ontologies. Lecture Notes in Artificial Intelligence, 2473:251–263, 2002.

[9] Gerd Stumme and Alexander Mädche. FCA-merge: bottom-up merging of ontologies. In Proc. 17th IJCAI, Seattle (WA US), pages 225–230, 2001.

[10] Petko Valtchev. Construction automatique de taxonomies pour l’aide à la représentation de connaissances par objets. Thèse d’informatique, Université Grenoble 1, 1999.

[11] Jérôme Euzenat and Petko Valtchev. An integrative proximity measure for ontology alignment. In Proc. ISWC-2003 workshop on semantic information integration, pages 33–38, Sanibel Island (FL US), 2003.

[12] Jérôme Euzenat and Petko Valtchev. Similarity-based ontology alignment in OWL-lite. In Proc. 15th ECAI, pages 333–337, Valencia (ES), 2004.



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