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

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

Dynamic personalized orders demand and uncertain manufacturing resource availability
have become the research hotspots of intelligent resource optimization allocation. Currently …

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) …

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 …

Multi-source knowledge fusion is one of the important research topics in the fields of artificial
intelligence, natural language processing, and so on. The research results of multi-source …

Knowledge Graphs are organized to describe entities from any discipline and the
interrelations between them. Apart from facilitating the inter-connectivity of datasets in the …

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the
unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively …

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of
downstream tasks such as summarization, link prediction, information retrieval, and question …

Abstract Knowledge Graphs (KGs) are composed of structured information about a particular
domain in the form of entities and relations. In addition to the structured information KGs help …