Tversky’s feature-based similarity and beyond

Tracking #: 1667-2879

This paper is currently under review
Silvia Likavec
Ilaria Lombardi
Federica Cena

Responsible editor: 
Lora Aroyo

Submission type: 
Full Paper
Similarity is one of the most straightforward ways to relate two objects and guide the human perception of the world. It has an important role in many areas, such as Information Retrieval, Natural Language Processing (NLP), Semantic Web and Recommender Systems. To help applications in these areas achieve satisfying results in finding similar concepts, it is important to simulate human perception of similarity and assess which similarity measure is the most adequate. In this work we wanted to gain some insights into Tversky’s feature-based semantic similarity measure on instances in a specific ontology. We experimented with various variations of this measure trying to improve its performance. We propose Normalised common-squared Jaccard’s similarity as an improvement of Tversky’s similarity measure. We also explored the performance of some hierarchy-based approaches and showed that feature-based approaches outperform them on two specific ontologies we tested. Above all, the combination of feature-based with hierarchy-based approaches shows the best performance on our datasets. We performed two separate evaluations. The first evaluation includes 137 subjects and 25 pairs of concepts in the recipes domain and the second one includes 147 subjects and 30 pairs of concepts in the domain of drinks. To our knowledge these are some of the most extensive evaluations performed in the field.
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