Abstract
Creativity has been a main target of artificial intelligence since its beginning and is still a major challenge today. One well-known option in computational creativity research is conceptual blending where new concepts are created through a selective combination of known ideas. However, existing approaches implementing blending are often either neglecting conceptual aspects, as in image morphing, or they suffer from the high complexity of the creation of the blend. Therefore, we propose a new neuro-symbolic approach to conceptual blending of ontologies based on knowledge graph embeddings which addresses both of these shortcomings. The inherent structure of the embedding space is used both to identify a generic space and to guide the blending process by interpreting blending as path search in the embedding space and by iteratively relaxing the input concepts. This is accomplished by combining a symbolic system for determining a step-wise refinement and analyzing the suitability of these refinements with the help of the embedding. We give an overview on the method and showcase possible heuristics.