892 research outputs found
5* Knowledge Graph Embeddings with Projective Transformations
Performing link prediction using knowledge graph embedding (KGE) models is a
popular approach for knowledge graph completion. Such link predictions are
performed by measuring the likelihood of links in the graph via a
transformation function that maps nodes via edges into a vector space. Since
the complex structure of the real world is reflected in multi-relational
knowledge graphs, the transformation functions need to be able to represent
this complexity. However, most of the existing transformation functions in
embedding models have been designed in Euclidean geometry and only cover one or
two simple transformations. Therefore, they are prone to underfitting and
limited in their ability to embed complex graph structures. The area of
projective geometry, however, fully covers inversion, reflection, translation,
rotation, and homothety transformations. We propose a novel KGE model, which
supports those transformations and subsumes other state-of-the-art models. The
model has several favorable theoretical properties and outperforms existing
approaches on widely used link prediction benchmarks
Correspondences between projective planes
We characterize integral homology classes of the product of two projective
planes which are representable by a subvariety.Comment: Improved readability, 14 page
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