Measuring relations between concepts in conceptual spaces

Autor(en): Bechberger, L.
Kühnberger, K.-U. 
Herausgeber: Petridis, M.
Bramer, M.
Stichwörter: Computer science; Computers; Conceptual spaces; Different domains; Fuzzy sets; Fuzzy sets, Betweenness; High dimensional spaces; Mathematical definitions; Measure; Subsethood, Artificial intelligence
Erscheinungsdatum: 2017
Herausgeber: Springer Verlag
Enthalten in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band: 10630 LNAI
Startseite: 87
Seitenende: 100
Zusammenfassung: 
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a high-dimensional space and concepts are represented by regions in this space. Our recent mathematical formalization of this framework is capable of representing correlations between different domains in a geometric way. In this paper, we extend our formalization by providing quantitative mathematical definitions for the notions of concept size, subsethood, implication, similarity, and betweenness. This considerably increases the representational power of our formalization by introducing measurable ways of describing relations between concepts. © Springer International Publishing AG 2017.
Beschreibung: 
Conference of 37th SGAI International Conference on Artificial Intelligence, AI 2017 ; Conference Date: 12 December 2017 Through 14 December 2017; Conference Code:207949
ISBN: 9783319710778
ISSN: 03029743
DOI: 10.1007/978-3-319-71078-5_7
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037730611&doi=10.1007%2f978-3-319-71078-5_7&partnerID=40&md5=914b4ed4a401e03d300a06a2a47bf690

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