As a powerful expression of human knowledge in a structural form, knowledge graph (KG)
has drawn great attention from both the academia and the industry and a large number of …

Question answering (QA) systems retrieve the most relevant answer to a natural language
question. Knowledge base question answering (KBQA) systems explore entities 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 …

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 …

Structured knowledge is an important backend in the Wikimedia ecosystem, and knowledge
graphs (KGs) like Wikidata are an asset also in many other applications like web search and …

At the heart of smart manufacturing is real-time semi-automatic decision-making. Such
decisions are vital for optimizing production lines, eg, reducing resource consumption …

Abstract Knowledge bases (KBs) about notable entities and their properties are an important
asset in applications such as search, question answering and dialog. All popular KBs …

Abstract Knowledge graphs are important in human-centered AI because of their ability to
reduce the need for large labelled machine-learning datasets, facilitate transfer learning …

Abstract Knowledge graphs (KGs) express relationships between entity pairs, and many real-
life problems can be formulated as knowledge graph reasoning (KGR). Conventional …

Machine knowledge about entities and their relationships has been a long-standing goal for
AI researchers. Over the last 15 years, thousands of public knowledge graphs have been …