Optimizing ontology alignments using flow based approach

Optimizing ontology alignments using flow based approach

Chahira Touati  Moussa Benaissa Yahia Lebbah 

Université d’Oran 1 Ahmed Ben Bella, B.P. 1524 El-M’Naouar, 31000 Oran, Algérie

Corresponding Author Email: 
chahira40@yahoo.fr, moussabenaissa@yahoo.fr, ylebbah@yahoo.fr
31 December 2016
| Citation



Ontologies have been created to solve the problem of the heterogeneity of data on the Web and to share domain knowledge between systems. However, several ontologies of the same domain are developed on the Web which became themselves source of heterogeneity. The Ontology alignment is a solution to solve this type of problem. It aims to discover the semantic correspondences between ontologies. We present in this paper an efficient graph-based approach to tackle the problem of extracting ontology alignment. More precisely our approach consists in modeling the problem of extracting an alignment (matching) which satisfies multiple cardinality constraints, as minimizing some cost on a flow network. Our approach has been evaluated on a variety of synthetic and real data, and compared with current used algorithms (e.g., Hungarian and Karp algorithms).


ontologies, ontology alignment, graph based approach, cardinality constraints, flow network.

1. Introduction
2. Travaux connexes
3. Préliminaires sur l’alignement des ontologies
4. Algorithmes utilisés dans notre approche
5. Contribution
6. Expérimentation et discussion
7. Conclusion

Bagher H., Abolhassani H. et Sayyadi H. (2006). A neural networks based approach for ontology alignment. In The 3rd international conference on soft computing and intelligent systems and the 7th international symposium on advanced intelligent systems.

Bellahsene Z., Bonifati A. et Rahm E. (2011). Schema matching and mapping. Springer.

Bodenreider O., Hayamizu T., Ringwald M., Coronado S. D. et Zhang S. (2005). Of mice and men: Aligning mouse and human anatomies. In Proceedings of american medical informatics association (aima) annual symposium, p. 61-65.

Chondrogiannis E., Andronikou V., Karanastasis E. et Varvarigou T. A. (2014). An intelligent ontology alignment tool dealing with complicated mismatches. In Proceedings of the 7th international workshop on semantic web applications and tools for life sciences, berlin, germany, december 9-11, 2014. Consulté sur http://ceur-ws.org/Vol-1320/paper_16.pdf

Cruz I., Antonelli F. et Stroe C. (2009). Efficient selection of mappings and automatic qualitydriven combination of matching methods. In International workshop on ontology matching, p. 49-60.

David J., Guillet F. et Briand H. (2007). Association rule ontology matching approach. International Journal of Semantic Web Information Systems, vol. 3, no 2, p. 27-49.

Do H. et Rahm E. (2007). Matching large schemas: Approaches and evaluation. Information Systems, vol. 32, no 6, p. 857–885.

Eckert K., Meilicke C. et Stuckenschmidt H. (2009, June). Improving ontology matching using meta-level learning. In The semantic web: Research and applications, 6th european semantic web conference, ESWC 2009, heraklion, crete, greece, may 31-june 4, 2009, proceedings, p. 158-172.

Euzenat J. et Shvaiko P. (2013). Ontology matching. Springer.

Euzenat J. et Valtchev P. (2004). Similarity-based ontology alignment in OWL-lite. In 16th european conference on artificial intelligence (ecai), p. 333-337. IOS Press.

Ford L. R. et Fulkerson D. R. (1962). Flows in networks. Princeton University Press.

Giunchiglia F., Yatskevich M. et Shvaiko P. (2007). Semantic matching: Algorithms and implementation. Journal of Data Semantics, vol. 4604, p. 1-38.

Hayamizu T., Mangan M., Corradi J., Kadin J. A. et Ringwald M. (2005). The adult mouse anatomical dictionary: a tool for annotating and integrating data. Genome Biology. Consulté sur http://www.ncbi.nlm.nih.gov/pubmed/15774030?dopt=Abstract

Ichise R. (2008). Machine learning approach for ontology mapping using multiple concept similarity measures. In 7th IEEE/ACIS international conference on computer and information science, IEEE/ACIS ICIS 2008, 14-16 may 2008, portland, oregon, USA, p. 340-346.

Jean-Mary Y., Shironoshita E. et Kabuka M. (2009). Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web, vol. 7, no 3.

Jiménez-Ruiz E., Grau B. C., Zhou Y. et Horrocks I. (2012). Large-scale interactive ontology matching: Algorithms and implementation. In ECAI 2012 - 20th european conference on artificial intelligence. including prestigious applications of artificial intelligence (PAIS-2012)system demonstrations track, montpellier, france, august 27-31 , 2012, p. 444-449.

Jiménez-Ruiz E. et Grau B. C. (2011). Logmap: Logic-based and scalable ontology matching. In International semantic web conference, lncs, vol. 7031, p. 273-288. Springer.

Kalfoglou Y. et Schorlemmer M. (2003, janvier). Ontology mapping: The state of the art. The Knowledge Engineering Review, vol. 18, no 1, p. 1-31.

Karp R. M. (1980). An algorithm to solve the m * n assignment problem in expected time o(mn log n). Networks, p. 143-152.

Kengue F. D. J., Euzenat J. et Valtchev P. (2008). Alignement d’ontologies dirigé par la structure. In Y. A. Ameur (Ed.), Cal, vol. RNTI-L-2, p. 155. Cépadués-Éditions.

Kuhn H. w. (1955). The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly, vol. 2, no 1-2, p. 83-97.

Liu J. (2003). Algorithms for minimum cost flows. The University of Western Ontario.

Mao M., Peng Y. et Spring M. (2008). A harmony based adaptive ontology mapping approach. In Proceedings of the 2008 international conference on semantic web web services, SWWS 2008, july 14-17, 2008, las vegas, nevada, usa, p. 336-342.

Martinez-Gil J., Alba E. et Montes J. A. (2008). Optimizing ontology alignments by using genetic algorithms. In Proceedings of the first international workshop on nature inspired reasoning for the semantic web, aachen, germany, october 27, vol. 419, p. 1-15. CEURWS.org.

Meillicke C. et Stuckenschmidt H. (2007). Applying logical constraints to ontology matching. In The 30th german conference on artificial intelligence, vol. 4667, p. 99-113. Springer. 

Meillicke C. et Stuckenschmidt H. (2015). New paradigm for ontology alignment. In In the proceedings of the tenth international workshop on ontology matching, collocated with the 14th international semantic web conference, ceur-ws, vol. 1545, p. 1-12. Bethlehem, PA, USA, CEUR-WS.org.

Noy N. (2004). Semantic integration: A survey of ontology-based approaches. SIGMOD Record, vol. 33, no 4, p. 65-70.

Noy N. et Musen M. A. (2002). The prompt suite: Interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies, vol. 59, no 6, p. 983-1024.

Rahm E. et Bernstein P. A. (2001, décembre). A survey of approaches to automatic schema matching. The VLDB Journal, vol. 10, no 4, p. 334-350.

Rosoiu M. E., Santos C. T. dos et Euzenat J. (2011). Ontology matching benchmarks: generation and evaluation. In 6th iswc workshop on ontology matching (om), p. 73-84.

Shvaiko P. et Euzenat J. (2005a). A survey of schema-based matching approaches. Journal on Data Semantics, vol. 4, p. 146-171.

Shvaiko P. et Euzenat J. (2005b). Tutorial on schema and ontology matching. Consulté sur http://disi.unitn.it/~accord/Presentations/ESWC’05-MatchingHandOuts.pdf(le20/04/2014)

Shvaiko P. et Euzenat J. (2008). Ten challenges for ontology matching. In Proceedings of the seventh international conference on ontologies, data bases and applications of semantics, vol. 5332, p. 1164-1182. Berlin, Heidelberg, Springer-Verlag.

Sioutos N., Coronado S. de et Haber M. (2007). Nci thesaurus: A semantic model integrating cancer-related clinical and molecular information. Journal of Biomedical Informatics, p. 30-43.

Xingsi X., YupingW. etWeichen H. (2015). Optimizing ontology alignments by using nsga-ii. International Arab Journal of Information Technology, vol. 12, no 2.

Zhang S. et Bodenreider O. (2007). Experience in aligning anatomical ontologies. International Journal on Semantic Web and Information Systems, vol. 3, no 2, p. 1-26.