Mining valuable hidden knowledge from large-scale data relies on the support of reasoning
technology. Knowledge graphs, as a new type of knowledge representation, have gained …

Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …

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

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

Abstract Knowledge base completion (KBC) aims to automatically infer missing facts by
exploiting information already present in a knowledge base (KB). A promising approach for …

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

Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs.
Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) …

The knowledge graph (KG) that represents structural relations among entities has become
an increasingly important research field for knowledge-driven artificial intelligence. In this …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

Hyper-relational knowledge graphs (KGs)(eg, Wikidata) enable associating additional key-
value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this …