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 …

Recently, increasing efforts are put into learning continual representations for symbolic
knowledge bases (KBs). However, these approaches either only embed the data-level …

Ontologies are widely used for representing domain knowledge and meta data, playing an
increasingly important role in Information Systems, the Semantic Web, Bioinformatics and …

This paper is concerned with knowledge graph embedding with background knowledge,
taking the formal perspective of logics. In knowledge graph embedding, knowledge …

OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been
widely used for knowledge representation. Similar to Knowledge Graphs (KGs), ontologies …

Neural networks and in general subsymbolic learning approaches perform well on usual
learning tasks, but they are black boxes lacking desired properties such as explainability or …

This paper presents sandra, a neuro-symbolic reasoner combining vectorial representations
with deductive reasoning. Sandra builds a vector space constrained by an ontology and …

Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard.
We explain how knowledge graph embeddings can be used to approximate probabilistic …

Query embedding approaches answer complex logical queries over incomplete knowledge
graphs (KGs) by computing and operating on low-dimensional vector representations of …

Recent approaches for knowledge-graph embeddings aim at connecting quantitative data
structures used in machine learning to the qualitative structures of logics. Such embeddings …