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 …

One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop
logical reasoning over the facts captured by a knowledge graph (KG). This problem is …

Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …

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 …

Abstract Knowledge Graph Completion (KGC) is a fundamental problem for temporal
knowledge graphs (TKGs), and TKGs embedding methods are one of the essential methods …

Automating ontology construction and curation is an important but challenging task in
knowledge engineering and artificial intelligence. Prediction by machine learning …

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 …

Abstract Knowledge graph (KG) embedding models have emerged as powerful means for
KG completion. To learn the representation of KGs, entities and relations are projected in a …