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Knowledge graphs comprise structural and textual information to represent knowledge. To predict new structural knowledge, current approaches learn representations using both types of information through knowledge graph embeddings and language models. These approaches commit to a single pre-trained language model. We hypothesize that heterogeneous language models may provide complementary information not exploited by current approaches. To investigate this hypothesis, we propose a unified framework that integrates multiple representations of structural knowledge and textual information. Our approach leverages hypercomplex algebra to model the interactions between (i) graph structural information and (ii) multiple text representations. Specifically, we utilize Dihedron models with 4*D dimensional hypercomplex numbers to integrate four different representations: structural knowledge graph …