Exploiting Visual Similarities for Ontology Alignment

@inproceedings{Doulaverakis2015ExploitingVS,
  title={Exploiting Visual Similarities for Ontology Alignment},
  author={Charalampos Doulaverakis and Stefanos Vrochidis and Yiannis Kompatsiaris},
  booktitle={International Conference on Knowledge Engineering and Ontology Development},
  year={2015}
}
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… 

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References

SHOWING 1-10 OF 23 REFERENCES

Using the Semantic Web as Background Knowledge for Ontology Mapping

A novel approach to ontology mapping is described, which is able to avoid this limitation by using background knowledge and has a high precision and also that it can find mappings, which are typically missed by existing approaches.

A Survey of Exploiting WordNet in Ontology Matching

This paper provides an overview of how to apply WordNet in the ontology matching research area and shows how the WordNet thesauri can support improving similarity measures.

Extending an ontology alignment system with BioPortal: a preliminary analysis

This paper examines two practical uses of BIOPORTAL as a generalized yet also specialized background knowledge source for the biomedical domain and provides a preliminary investigation of the results of these two uses using the LogMap system.

A String Metric for Ontology Alignment

A new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems is presented.

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

This paper presents a novel methodology for automatically selecting background knowledge sources for any given ontologies to match, and measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which is called the mapping gain.

An API for Ontology Alignment

A format for expressing alignments in RDF is presented, which can be seen as an extension of the OWL API and shares some design goals with it, and how this API can be used for effectively aligning ontologies and completing partial alignments, thresholding alignments or generating axioms and transformations is shown.

Results of the Ontology Alignment Evaluation Initiative 2007

This paper is an overall presentation of the OAEI 2010 campaign, which introduces a new evaluation modality in association with the SEALS project which provides more automation to the evaluation and more direct feedback to the participants.

Ontology matching with semantic verification

Efficient Selection of Mappings and Automatic Quality-driven Combination of Matching Methods

An optimized method that performs the selection of mappings given the similarities between entities computed by any matching algorithm, a threshold value, and the desired cardinalities of the mappings is presented.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution and the supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment.