Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

Complex biological systems are traditionally modelled as graphs of interconnected
biological entities. These graphs, ie biological knowledge graphs, are then processed using …

Knowledge Graphs (KGs) have found many applications in industrial and in academic
settings, which in turn, have motivated considerable research efforts towards large-scale …

Knowledge graphs are structured representations of real world facts. However, they typically
contain only a small subset of all possible facts. Link prediction is a task of inferring missing …

Knowledge graph (KG) embeddings learn low-dimensional representations of entities and
relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which …

In this work, we move beyond the traditional complex-valued representations, introducing
more expressive hypercomplex representations to model entities and relations for …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Abstract Query embedding (QE)---which aims to embed entities and first-order logical (FOL)
queries in low-dimensional spaces---has shown great power in multi-hop reasoning over …

Graph embedding methods produce unsupervised node features from graphs that can then
be used for a variety of machine learning tasks. However, modern graph datasets contain …