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Rahnama, Javad and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (5. June 2020): Learning Tversky Similarity. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Vol. 1238 Cham: Springer. pp. 269-280

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Abstract

In this paper, we advocate Tversky’s ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.

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