The cognitive theory of conceptual blending models human creativity as a mental process that combines two mental spaces into a new mental space, called a blend. According to this theory, 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 their non-common structure in a selective way to the novel blended space. In this paper, we apply this idea to blend input spaces modeled as complex EL⁺⁺ concepts. To construct the generic space of two EL⁺⁺ concepts, these need to be generalised in a controlled manner. For this, we propose an upward refinement operator that is used for finding common generalisations of EL⁺⁺ concepts.

Conceptual blending is understood to be a process that serves a variety of cognitive purposes, including creativity, and has been highly influential in cognitive linguistics. In this line of thinking, human creativity is modeled as a blending process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinatorial creativity, a blend is constructed by taking the existing commonalities among the input mental spaces-known as the generic space-into account, and by projecting their structure in a selective way. 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 ε葦⁺⁺ and propose an upward refinement operator for generalising ε葦⁺⁺ 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. We exemplify our approach in the domain of computer icons.