Exploiting Visual Similarities for Ontology Alignment

Charalampos Doulaverakis, Stefanos Vrochidis, Ioannis Kompatsiaris


Ontology alignment is the process where two different ontologies that usually describe similar domains are ’aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods and metrics that measure semantic equivalence have been proposed in literature. The most common features that these metrics employ are string-, lexical- , structure- and semantic-based similarities for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity in cases where images are associated with concepts. Nowadays the existence of several resources (e.g. ImageNet) that map lexical concepts onto images allows for exploiting visual similarities for this purpose. In this paper, a novel approach for ontology matching based on visual similarity is presented. Each ontological entity is associated with sets of images, retrieved through ImageNet or web-based search, and state of the art visual feature extraction, clustering and indexing for computing the similarity between entities is employed. An adaptation of a popular Wordnet-based matching algorithm to exploit the visual similarity is also proposed. Our method is compared with traditional metrics against a standard ontology alignment benchmark dataset and demonstrates promising results.


  1. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346-359.
  2. Chatfield, K. and Zisserman, A. (2013). VISOR: Towards on-the-fly large-scale object category retrieval. In Asian Conference of Computer Vision - ACCV 2012, pages 432-446. Springer Berlin Heidelberg.
  3. Chen, X., Xia, W., Jiménez-Ruiz, E., and Cross, V. (2014). Extending an ontology alignment system with bioportal: a preliminary analysis. In Poster at Intl Sem. Web Conf.(ISWC).
  4. Cruz, I. F., Palandri Antonelli, F., and Stroe, C. (2009). Efficient selection of mappings and automatic qualitydriven combination of matching methods. In ISWC International Workshop on Ontology Matching (OM) CEUR Workshop Proceedings, volume 551, pages 49- 60. Citeseer.
  5. Dragisic, Z., Eckert, K., Euzenat, J., Faria, D., Ferrara, A., Granada, R., Ivanova, V., Jimenez-Ruiz, E., Kempf, A., Lambrix, P., et al. (2014). Results of the ontology alignment evaluation initiative 2014. In International Workshop on Ontology Matching, pages 61-104.
  6. Euzenat, J. (2004). An API for ontology alignment. In The Semantic Web-ISWC 2004, pages 698-712. Springer.
  7. Faria, D., Pesquita, C., Santos, E., Cruz, I. F., and Couto, F. M. (2014). Automatic background knowledge selection for matching biomedical ontologies.
  8. ONE, 9(11):e111226.
  9. Faria, D., Pesquita, C., Santos, E., Palmonari, M., Cruz, I. F., and Couto, F. M. (2013). The agreementmakerlight ontology matching system. In On the Move to Meaningful Internet Systems: OTM 2013 Conferences, pages 527-541. Springer.
  10. Jean-Mary, Y. R., Shironoshita, E. P., and Kabuka, M. R. (2009). Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web, 7(3):235-251.
  11. Jégou, H., Douze, M., Schmid, C., and Pérez, P. (2010). Aggregating local descriptors into a compact image representation. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 3304- 3311. IEEE.
  12. Kirsten, T., Gross, A., Hartung, M., and Rahm, E. (2011). Gomma: a component-based infrastructure for managing and analyzing life science ontologies and their evolution. J. Biomedical Semantics, 2(6).
  13. Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97.
  14. Lin, F. and Sandkuhl, K. (2008). A survey of exploiting wordnet in ontology matching. In Artificial Intelligence in Theory and Practice II, pages 341-350. Springer.
  15. Melnik, S., Garcia-Molina, H., and Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Data Engineering, 2002. Proceedings. 18th International Conference on, pages 117-128. IEEE.
  16. Miller, G. A. (1995). WORDNET: a Lexical Database for English. Communications of the ACM, 38(11):39-41.
  17. Ngo, D. and Bellahsene, Z. (2012). YAM++: A multistrategy based approach for ontology matching task. In Knowledge Engineering and Knowledge Management, pages 421-425. Springer.
  18. Pesquita, C., Faria, D., Santos, E., Neefs, J.-M., and Couto, F. M. (2014). Towards visualizing the alignment of large biomedical ontologies. In Data Integration in the Life Sciences, pages 104-111. Springer.
  19. Sabou, M., d'Aquin, M., and Motta, E. (2006). Using the semantic web as background knowledge for ontology mapping. In In Proc. of the Int. Workshop on Ontology Matching (OM-2006.
  20. Shvaiko, P. and Euzenat, J. (2005). A survey of schemabased matching approaches. In Journal on Data Semantics IV, pages 146-171. Springer.
  21. Spyromitros-Xioufis, E., Papadopoulos, S., Kompatsiaris, I., Tsoumakas, G., and Vlahavas, I. (2014). A comprehensive study over VLAD and product quantization in large-scale image retrieval. IEEE Transactions on Multimedia, 16(6):1713-1728.
  22. Stoilos, G., Stamou, G., and Kollias, S. (2005). A string metric for ontology alignment. In Gil, Y., editor, Proceedings of the International Semantic Web Conference (ISWC 05), volume 3729 of LNCS, pages 624- 637. Springer-Verlag.
  23. Wu, Z. and Palmer, M. (1994). Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pages 133-138. Association for Computational Linguistics.

Paper Citation

in Harvard Style

Doulaverakis C., Vrochidis S. and Kompatsiaris I. (2015). Exploiting Visual Similarities for Ontology Alignment . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 29-37. DOI: 10.5220/0005588200290037

in Bibtex Style

author={Charalampos Doulaverakis and Stefanos Vrochidis and Ioannis Kompatsiaris},
title={Exploiting Visual Similarities for Ontology Alignment},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Exploiting Visual Similarities for Ontology Alignment
SN - 978-989-758-158-8
AU - Doulaverakis C.
AU - Vrochidis S.
AU - Kompatsiaris I.
PY - 2015
SP - 29
EP - 37
DO - 10.5220/0005588200290037