A comprehensive overview of knowledge graph completion
T Shen, F Zhang, J Cheng - Knowledge-Based Systems, 2022 - Elsevier
Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …
downstream knowledge-aware tasks (such as recommendation and intelligent question …
Research commentary on recommendations with side information: A survey and research directions
Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …
overload problem in recent decades. Traditional recommender systems, however, suffer …
Knowledge graph embedding: A survey of approaches and applications
Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
[PDF][PDF] Representation learning of knowledge graphs with hierarchical types.
R ** for domain independent entity linking
Neural entity linking models are very powerful, but run the risk of overfitting to the domain
they are trained in. For this problem, a “domain” is characterized not just by genre of text but …
they are trained in. For this problem, a “domain” is characterized not just by genre of text but …
Label noise reduction in entity ty** by heterogeneous partial-label embedding
Current systems of fine-grained entity ty** use distant supervision in conjunction with
existing knowledge bases to assign categories (type labels) to entity mentions. However, the …
existing knowledge bases to assign categories (type labels) to entity mentions. However, the …