Abstract Knowledge graph embeddings that generate vector space representations of
knowledge graph triples, have gained considerable popularity in past years. Several …

Abstract Knowledge graph embedding models (KGEMs) developed for link prediction learn
vector representations for entities in a knowledge graph, known as embeddings. A common …

This paper develops an innovative method that enables neural networks to generate and
utilize knowledge graphs, which describe their concept-level knowledge and optimize …

Community-based knowledge graphs are generated following hybrid approaches, where
human intelligence empowers computational methods to effectively integrate encyclopedic …

Wikidata, a community-maintained knowledge graph (KG), may integrate different entities
and properties with the same meaning. Contributors can add new properties similar in …

Abstract Knowledge Graph Embeddings Models project entities and relations from
Knowledge Graphs into a vector space. Despite their widespread application, concerns …

Knowledge graphs (KGs) provide structured representation of data in the form of relations
between different entities. The semantics of relations between words and entities are often …

Abstract Knowledge graphs have become an essential source of entity-centric information
for modern applications. Today's KGs have reached a size of billions of RDF triples extracted …

Abstract Knowledge graphs are repositories providing factual knowledge about entities.
They are a great source of knowledge to support modern AI applications for Web search …

Knowledge graphs are structured repositories of knowledge that store facts about the
general world or a particular domain in terms of entities and their relationships. Owing to the …