  Knowledge Graphs, such as Wikidata, comprise structural and textual knowledge
in order to represent knowledge. For each of the two modalities dedicated
approaches for graph embedding and language models learn patterns that allow
for predicting novel structural knowledge. Few approaches have integrated
learning and inference with both modalities and these existing ones could only
partially exploit the interaction of structural and textual knowledge. In our
approach, we build on existing strong representations of single modalities and
we use hypercomplex algebra to represent both, (i), single-modality embedding
as well as, (ii), the interaction between different modalities and their
complementary means of knowledge representation. More specifically, we suggest
Dihedron and Quaternion representations of 4D hypercomplex numbers to integrate
four modalities namely structural knowledge graph embedding, word-level
representations (e.g.\ Word2vec, Fasttext), sentence-level representations
(Sentence transformer), and document-level representations (sentence
transformer, Doc2vec). Our unified vector representation scores the
plausibility of labelled edges via Hamilton and Dihedron products, thus
modeling pairwise interactions between different modalities. Extensive
experimental evaluation on standard benchmark datasets shows the superiority of
our two new models using abundant textual information besides sparse structural
knowledge to enhance performance in link prediction tasks.
