Sigmoid similarity - a new feature-based similarity measure

Similarity is one of the most straightforward ways to relate objects and guide the human perception of the world. It has an important role in many areas, such as Information Retrieval, Natural Language Processing, Semantic Web and Recommender Systems. To help applications in these areas achieve satisfying results when finding similar concepts, it is important to simulate human perception of similarity and assess which similarity measure is the most adequate. We propose Sigmoid similarity, a feature-based semantic similarity measure on instances in a specific ontology, as an improvement of Dice measure. 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 the best of our knowledge these are some of the most extensive evaluations in the field. We also explored the performance of some hierarchy-based approaches and showed that feature-based approaches outperform them on two specific ontologies we tested. In addition, we tried to incorporate hierarchy-based information into our measures and concluded it is not worth complicating the measures only based on features with additional information since they perform comparably.