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A Visual Similarity Metric for Ontology Alignment

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 631))

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Abstract

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 have been proposed in literature. The most common features that these methods employ are string-, lexical-, structure- and semantic-based features for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity. Nowadays the existence of several resources that map lexical concepts onto images allows for exploiting visual features for this purpose. In this paper, a novel method, defining a visual-based similarity metric for ontology matching, is presented. Each ontological entity is associated with sets of images. State of the art visual feature extraction, clustering and indexing for computing the visual-based similarity between entities is employed. An adaptation of a Wordnet-based matching algorithm to exploit the visual similarity is also proposed. The proposed visual similarity approach is compared with standard metrics and demonstrates promising results.

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Notes

  1. 1.

    CIDOC-CRM, http://www.cidoc-crm.org.

  2. 2.

    Europeana Data Model, http://labs.europeana.eu.

  3. 3.

    OAEI, http://oaei.ontologymatching.org.

  4. 4.

    ImageNet, http://www.image-net.org/.

  5. 5.

    Flickr, https://www.flickr.com/.

  6. 6.

    Yahoo search, https://images.search.yahoo.com.

  7. 7.

    OAEI 2014, http://oaei.ontologymatching.org/2014.

  8. 8.

    ImageNet visual features download,

    http://image-net.org/download-features.

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Acknowledgements

This work was supported by MULTISENSOR (contract no. FP7-610411) and KRISTINA (contract no. H2020-645012) projects, partially funded by the European Commission.

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Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Charalampos Doulaverakis, Stefanos Vrochidis & Ioannis Kompatsiaris

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  1. Charalampos Doulaverakis
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  2. Stefanos Vrochidis
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  3. Ioannis Kompatsiaris
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Correspondence to Charalampos Doulaverakis .

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Editors and Affiliations

  1. Instituto de Telecomunicações/IST, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Delft, The Netherlands

    Jan L.G. Dietz

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. University of Reading, Reading, United Kingdom

    Kecheng Liu

  5. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Doulaverakis, C., Vrochidis, S., Kompatsiaris, I. (2016). A Visual Similarity Metric for Ontology Alignment. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2015. Communications in Computer and Information Science, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-319-52758-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-52758-1_11

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