Query2box: Reasoning over knowledge graphs in vector space using box embeddings
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a
fundamental yet challenging task. Recently, a promising approach to this problem has been …
fundamental yet challenging task. Recently, a promising approach to this problem has been …
Knowledge graph embeddings and explainable AI
Abstract Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector spaces. In this …
representation in which entities and relationships are embedded in vector spaces. In this …
Convolutional complex knowledge graph embeddings
We investigate the problem of learning continuous vector representations of knowledge
graphs for predicting missing links. Recent results suggest that using a Hermitian inner …
graphs for predicting missing links. Recent results suggest that using a Hermitian inner …
Benchmark and best practices for biomedical knowledge graph embeddings
Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text
and medical codes. There is a wealth of expert-curated biomedical domain knowledge …
and medical codes. There is a wealth of expert-curated biomedical domain knowledge …
Bique: Biquaternionic embeddings of knowledge graphs
J Guo, S Kok - arXiv preprint arXiv:2109.14401, 2021 - arxiv.org
Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs
(KGs). Existing KGE models rely on geometric operations to model relational patterns …
(KGs). Existing KGE models rely on geometric operations to model relational patterns …
Vector-valued distance and gyrocalculus on the space of symmetric positive definite matrices
F López, B Pozzetti, S Trettel… - Advances in Neural …, 2021 - proceedings.neurips.cc
We propose the use of the vector-valued distance to compute distances and extract
geometric information from the manifold of symmetric positive definite matrices (SPD), and …
geometric information from the manifold of symmetric positive definite matrices (SPD), and …
Revisiting evaluation of knowledge base completion models
Representing knowledge graphs (KGs) by learning embeddings for entities and relations
has led to accurate models for existing KG completion benchmarks. However, due to the …
has led to accurate models for existing KG completion benchmarks. However, due to the …
Hopfe: Knowledge graph representation learning using inverse hopf fibrations
Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to
represent entities and relations in a dense vector space and employed in downstream tasks …
represent entities and relations in a dense vector space and employed in downstream tasks …
Adversarial attacks on knowledge graph embeddings via instance attribution methods
Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about
the security vulnerabilities that might disrupt their intended behaviour. We study data …
the security vulnerabilities that might disrupt their intended behaviour. We study data …
Poisoning knowledge graph embeddings via relation inference patterns
We study the problem of generating data poisoning attacks against Knowledge Graph
Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison …
Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison …