Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

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

The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …

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 …

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

Wikidata has been increasingly adopted by many communities for a wide variety of
applications, which demand high-quality knowledge to deliver successful results. 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 …

Conventional representation learning algorithms for knowledge graphs (KG) map each
entity to a unique embedding vector. Such a shallow lookup results in a linear growth of …

Knowledge hypergraph embedding, which projects entities and n-ary relations into a low-
dimensional continuous vector space to predict missing links, remains a challenging area to …