Abstract
This paper proposes an ontology matching method for aligning a source ontology with target ontologies already published and linked on the Linked Open Data (LOD) cloud. This method relies on the refinement of a set of input alignments generated by existing ontology matching methods. Since the ontologies to be aligned can be expressed in several representation languages with different levels of expressiveness and the existing ontology matching methods can only be applied to some representation languages, the first step of our method consists in applying existing matching methods to as many ontology variants as possible. We then propose to apply two main strategies to refine the initial alignment set: the removal of different kinds of ambiguities between correspondences and the use of the links published on the LOD. We illustrate our proposal in the field of life sciences and environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
Since ambiguous correspondences according to type 1 produce only noises, the evaluation of the best recall is done considering \(\overset{agr*}{C}\) and not \(\overset{agr}{C}\).
References
Aguirre, J., et al. (2012). Results of the ontology alignment evaluation initiative 2012. In Proceedings of 7th ISWC Workshop on Ontology Matching (OM) (p. 73115).
Bernstein, P. A., Madhavan, J., & Rahm, E. (2011). Generic schema matching, ten years later. PVLDB, 4(11), 695–701.
Bizer, C. (2013). Interlinking scientific data on a global scale. Data Science Journal, 12, GRDI6–GRDI12.
Buche, P., et al. (2013). Intégration de données hétérogènes et imprecise guide par une resource termino-ontologique. application au domaine des sciences du vivant. RSTI série Revue dIntelligence Artificielle, 27(4–5), 539–568.
Caracciolo, C., Stellato, A., Rajbhandari, S., Morshed, A., Johannsen, G., Keizer, J., et al. (2012). Thesaurus maintenance, alignment and publication as linked data: The AGROVOC use case. IJMSO, 7(1), 65–75.
Cruz, I. F., Palmonari, M., Caimi, F., & Stroe, C. (2011). Towards “on the go” matching of linked open data ontologies. In Workshop on Discovering Meaning On the Go in Large Heterogeneous Data 2011 (LHD-11), Barcelona, Spain, July 16, 2011.
David, J. (2007). AROMA: une méthode pour la découverte d’alignements orientés entre ontologies partir de règles d’association. Ph.D. thesis, Université de Nantes.
David, J., Euzenat, J., Scharffe, F., & Trojahn dos Santos, C. (2011). The alignment api 4.0. Semantic Web, 2(1):310.
Eckert, K., Meilicke, C., & Stuckenschmidt, H. (2009). Improving ontology matching using meta-level learning. In The semantic web: research and applications (Vol. 5554, pp. 158–172). Lecture notes in computer science. Berlin, Heidelberg: Springer.
Euzenat, J. (2008). Algebras of ontology alignment relations. In International Semantic Web Conference (Vol. 5318). Lecture notes in computer science. Heidelberg: Springer.
Euzenat, J., & Shvaiko, P. (2007). Ontology matching (Vol. 18). Heidelberg: Springer.
Ghoula, N., Nindanga, H., & Falquet, G. (2014). Opérateurs de gestion des alignements de ressources de connaissances hétérogènes (to be completed).
Grütze, T., Böhm, C., & Naumann, F. (2012). Holistic and scalable ontology alignment for linked open data. In C. Bizer, T. Heath, T. Berners-Lee, & M. Hausenblas (Eds.), WWW2012 Workshop on Linked Data on the Web, Lyon, France, 16 April, 2012 (Vol. 937). CEUR workshop proceedings. CEUR-WS.org.
Jain, P., Hitzler, P., Sheth, A. P., Verma, K., & Yeh, P. Z. (2010). Ontology alignment for linked open data. In Proceedings of the 9th International Semantic Web Conference on The Semantic Web - Volume Part I (pp. 402–417). Berlin, Heidelberg: Springer.
Jiménez-Ruiz, E., & Grau, B. C. (2011). LogMap: Logic-based and scalable ontology matching. In The Semantic WebISWC 2011 (pp. 273–288). Springer.
Lee, Y., Sayyadian, M., Doan, A., & Rosenthal, A. S. (2007). eTuner: Tuning schema matching software using synthetic scenarios. The VLDB Journal, 16(1), 97–122.
McCrae, J., Spohr, D., & Cimiano, P. (2011). Linking Lexical resources and ontologies on the semantic web with lemon. In G. Antoniou, M. Grobelnik, E. P. B. Simperl, B. Parsia, D. Plexousakis, P. D. Leenheer, & J. Z. Pan (Eds.), ESWC (1) (Vol. 6643, pp. 245–259). Lecture notes in computer science. Springer.
Mochol, M., & Jentzsch, A. (2008). Towards a rule-based matcher selection. In A. Gangemi & J. Euzenat (Eds.), Knowledge engineering: Practice and patterns (Vol. 5268, pp. 109–119). Lecture notes in computer science. Berlin, Heidelberg: Springer.
Mougin, F., & Grabar, N. (2013). Using a cross-language approach to acquire new mappings between two biomedical terminologies. In Artificial intelligence in medicine (Vol. 7885, pp. 221–226). Lecture notes in computer science. Berlin, Heidelberg: Springer.
Nikolov, A., Uren, V., Motta, E., & Roeck, A. (2009). Overcoming schema heterogeneity between linked semantic repositories to improve coreference resolution. In Proceedings of the 4th Asian Conference on The Semantic Web, ASWC 2009 (pp. 332–346). Berlin, Heidelberg: Springer.
Parundekar, R., Knoblock, C. A., & Ambite, J. L. (2012). Discovering concept coverings in ontologies of linked data sources. In P. Cudré-Mauroux, et al. (Eds.), International Semantic Web Conference (1) (Vol. 7649, pp. 427–443). Lecture notes in computer science. Springer.
Pernelle, N. & Sais, F. (2011). LDM: Link discovery method for new resource integration. In M.-E. V. Zoé Lacroix & Edna Ruckhaus (Eds.), Fourth International Workshop on Resource Discovery, Heraklion, Grèce (Vol. 737, pp. 94–108).
Rahm, E. (2011). Towards large-scale schema and ontology matching. In Z. Bellahsene, A. Bonifati & E. Rahm (Eds.), Schema Matching and Mapping (pp. 3–27). Springer.
Reymonet, A., Thomas, J., & Aussenac-Gilles, N. (2007). Modelling ontological and terminological resources in OWL DL. In OntoLex 2007—Workshop at ISWC07, Busan, South-Korea
Roche, C., Calberg-Challot, M., Damas, L., & Rouard, P. (2009). Ontoterminology—a new paradigm for terminology. In J. L. G. Dietz (Ed.), KEOD (pp. 321–326). INSTICC Press.
Shvaiko, P., & Euzenat, J. (2013). Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering, 25(1), 158–176.
Spiliopoulos, V., & Vouros, G. (2012). Synthesizing ontology alignment methods using the max-sum algorithm. IEEE Transactions on Knowledge and Data Engineering, 24(5), 940–951.
Steyskal, S., & Polleres, A. (2013). Mix ‘n’ match: An alternative approach for combining ontology matchers. In R Meersman, H. Panetto, T. S. Dillon, J. Eder, Z. Bellahsene, N. Ritter, P. D. Leenheer & D. Dou (Eds.), On the Move to Meaningful Internet Systems: OTM 2013 Conferences - Confederated International Conferences: CoopIS, DOA-Trusted Cloud, and ODBASE 2013, Graz, Austria, September 9–13, 2013. Proceedings (Vol. 8185, pp. 555–563). Lecture notes in computer science. Springer.
Stoilos, G., Stamou, G., & Kollias, S. (2005). A string metric for ontology alignment. In The Semantic Web—ISWC 2005 (pp. 624–637). Springer.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Hecht, T., Buche, P., Dibie, J., Ibanescu, L., dos Santos, C.T. (2017). Ontology Alignment Using Web Linked Ontologies as Background Knowledge. In: Guillet, F., Pinaud, B., Venturini, G. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 665. Springer, Cham. https://doi.org/10.1007/978-3-319-45763-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-45763-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45762-8
Online ISBN: 978-3-319-45763-5
eBook Packages: EngineeringEngineering (R0)