Upward refinement operators for conceptual blending in the description logic

Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend . According to thi...

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Bibliographic Details
Published in:Annals of mathematics and artificial intelligence Vol. 82; no. 1-3; pp. 69 - 99
Main Authors: Confalonieri, Roberto, Eppe, Manfred, Schorlemmer, Marco, Kutz, Oliver, Peñaloza, Rafael, Plaza, Enric
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.03.2018
Springer
Subjects:
Algorithms
Artificial Intelligence
Complex Systems
Computer Science
Creative ability
Mathematics
89.20.Ff
Conceptual blending
Computational creativity
Description logic
Answer set programming
07.05.Mh
ISSN:1012-2443, 1573-7470
Online Access:Get full text
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Summary:Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend . According to this form of combinational creativity , a blend is constructed by taking the commonalities among the input mental spaces into account, to form a so-called generic space , and by projecting the non-common structure of the input spaces in a selective way to the novel blended space. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic 𝓔 𝓛 + + and propose an upward refinement operator for generalising 𝓔 𝓛 + + concepts. We show how the generalisation operator is translated to Answer Set Programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. The generalisations obtained by the ASP process are used in a conceptual blending algorithm that generates and evaluates possible combinations of blends. We exemplify our approach in the domain of computer icons.
ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-016-9524-8