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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 and more specifically Jaccard’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 Sigmoid similarity as an improvement of Jaccard’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. We also tried to incorporate hierarchy-based information into our measures and, even though they do bring some slight improvement, it seems that it is not worth complicating the measures with this information, since the measures only based on features show very comparable performance. We performed two separate evaluations with real evaluators. 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 drinks domain. To our knowledge these are some of the most extensive evaluations performed in the field.