28 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
Faithiful Embeddings for EL++ Knowledge Bases
Recently, increasing efforts are put into learning continual representations
for symbolic knowledge bases (KBs). However, these approaches either only embed
the data-level knowledge (ABox) or suffer from inherent limitations when
dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model
the logical structure present in the KBs. We present BoxEL, a geometric KB
embedding approach that allows for better capturing the logical structure
(i.e., ABox and TBox axioms) in the description logic EL++. BoxEL models
concepts in a KB as axis-parallel boxes that are suitable for modeling concept
intersection, entities as points inside boxes, and relations between
concepts/entities as affine transformations. We show theoretical guarantees
(soundness) of BoxEL for preserving logical structure. Namely, the learned
model of BoxEL embedding with loss 0 is a (logical) model of the KB.
Experimental results on (plausible) subsumption reasonings and a real-world
application for protein-protein prediction show that BoxEL outperforms
traditional knowledge graph embedding methods as well as state-of-the-art EL++
embedding approaches.Comment: Published in ISWC'2