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

Compared to black-box neural networks, logic rules express explicit knowledge, can provide
human-understandable explanations for reasoning processes, and have found their wide …

Abstract Knowledge Graphs (KGs) currently contain a vast amount of structured information
in the form of entities and relations. Because KGs are often constructed automatically by …

In the last few years, we have witnessed the emergence of several knowledge graphs that
explicitly describe research knowledge with the aim of enabling intelligent systems for …

Abstract Knowledge Graph Completion seeks to find missing elements in a Knowledge
Graph, usually edges representing some relation between two concepts. One possible way …

Completion methods learn models to infer missing (subject, predicate, object) triples in
knowledge graphs, a task known as link prediction. The training phase is based on samples …

In link prediction evaluation, an embedding model assigns plausibility scores to unseen
triples in a knowledge graph using an input partial triple. Performance metrics like mean …

Data integration tasks such as the creation and extension of knowledge graphs involve the
fusion of heterogeneous entities from many sources. Matching and fusion of such entities …

Even though knowledge graphs have proven very useful for several tasks, they are marked
by incompleteness. Completion algorithms aim to extend knowledge graphs by predicting …

Abstract Enterprise and public Knowledge Graphs (KGs) are known to be incomplete.
Methods for automatic completion, sometimes by rule learning, scale well. While previous …