Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

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

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

Abstract Knowledge graphs contain knowledge about the world and provide a structured
representation of this knowledge. Current knowledge graphs contain only a small subset of …

Distance based knowledge graph embedding methods show promising results on link
prediction task, on which two topics have been widely studied: one is the ability to handle …

Over the recent years, embedding methods have attracted increasing focus as a means for
knowledge graph completion. Similarly, rule-based systems have been studied for this task …

With the rapid development of knowledge bases (KBs), link prediction task, which completes
KBs with missing facts, has been broadly studied in especially binary relational KBs (aka …

We present KBLRN, a framework for end-to-end learning of knowledge base
representations from latent, relational, and numerical features. KBLRN integrates feature …

Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG),
have become the crux of KG embedding. Lots of SFs, which target at capturing different …

Abstract Knowledge graphs are incomplete by nature, with only a limited number of
observed facts from the world knowledge being represented as structured relations between …