The notion of semantic similarity has been extensively explored in many different fields, including computer science. Various similarity models, along with specific measures that work within them, have been proposed over the years in many different contexts, and for both generic, and domain–specific applications. A holistic view of the area reveals problems that need to be addressed, including the lack of generic models that would be able to exploit full extent of modern data sources, rich with complex semantic descriptions, as well as the shortcomings in\ninterpretation of similarity scores.\nThe similarity framework SimDim presented in this thesis proposes a specific approach to\nsimilarity scoring that allows detailed comparisons of similarity between semantic objects in\nany domain. It includes the dimensional approach, which adds a layer of meaning to similarity scores themselves, allowing for more informed choice of measures for any given problem, and more awareness in interpretation and comparison of scores. Dimensions of similarity frame the notion of similarity in terms of the meaning of data, that was used to arrive at the result, and not, as it was done before, in terms of model of similarity, or the data format. The dimensional\nscore enables capturing of different kinds of similarity, and, therefore, makes the score more\ninformative.\nThe SimDim framework is based on a similarity model and a generic algorithm for semantic similarity, with implementation. The algorithm is a highly configurable and domain–independent\ntool that enables introduction of the dimensional approach into practice. It allows a deep view\nof objects and their features, that includes all available knowledge, or a subset of it, explicitly\nchosen to address specific tasks. It works within the similarity model, in which calculation of\nfeatures is encapsulated in concrete functions, that are “first class citizens”. Under this approach, the algorithm may be parametrized in many different ways, making use of various ways\nto calculate features. Particular configurations (i.e. parametrizations) of the algorithm result\nin specific measures, examples of which are included as well.\nThe parts of the SimDim framework – the dimensional approach, the model, and the algorithm\nwork in tandem to deliver an integrated solution to, and offer a different (to currently available\nsolutions) perspective on the problem of similarity calculation.
naukowcy
studenci
uczniowie
Dziedzina nauk inżynieryjno-technicznych / informatyka techniczna i telekomunikacja (2018)
Computing multidimensional semantic similarity in ontologies (PD-2021-04)
similarity
podobieństwo
Instytut Badań Systemowych PAN w Warszawie