http://id.e-science.pl/records/83087
type: Thesis
acceptance date: 2023-03-17
author info: AuthorInfo
  affiliation: http://id.e-science.pl/vocab/unit/6165
  author: http://id.e-science.pl/vocab/person/17489
  position: 1
collection: http://id.e-science.pl/vocab/collection/5
creation year: 2021
description: 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 diffe… @en
destination group: naukowcy
destination group: studenci
destination group: uczniowie
file info: FileInfo
  name: PD-2021-04.pdf
  uri: https://data.e-science.pl/83087/PD-2021-04.pdf
file info: FileInfo
  name: PD-2021-04.docx
  uri: https://data.e-science.pl/83087/PD-2021-04.docx
file info: FileInfo
  name: Computing_multidimensional_semantic_similarity_in_ontologies_-_Szmeja.latex.7z
  uri: https://data.e-science.pl/83087/Computing_multidimensional_semantic_similarity_in_ontologies_-_Szmeja.latex.7z
harmful content: false
keyword info: KeywordInfo
  name: similarity @en
  uri: http://id.e-science.pl/vocab/kv/24413
keyword info: KeywordInfo
  name: podobieństwo
  uri: http://id.e-science.pl/vocab/kv/24413
language: Angielski
license info: LicenseInfo
  name: CC BY-SA 4.0
  uri: https://creativecommons.org/licenses/by-sa/4.0/legalcode.pl
number of pages: 144
origin: AZON2
partner: Organization
  name: Instytut Badań Systemowych PAN w Warszawie
place of creation: Warszawa
scientific discipline: Dziedzina nauk inżynieryjno-technicznych / informatyka techniczna i telekomunikacja (2018)
submitter: Person
  name: Anna Wasilewska
supervisor: http://id.e-science.pl/vocab/person/17488
title: Computing multidimensional semantic similarity in ontologies (PD-2021-04) @en