Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Knowledge graph embedding (KGE) models represent each entity and relation of a
knowledge graph (KG) with low-dimensional embedding vectors. These methods have …

Abstract Knowledge base completion (KBC) aims to automatically infer missing facts by
exploiting information already present in a knowledge base (KB). A promising approach for …

Recently, knowledge graph embeddings (KGEs) have received significant attention, and
several software libraries have been developed for training and evaluation. While each of …

Most of todays work on knowledge graph completion is concerned with sub-symbolic
approaches that focus on the concept of embedding a given graph in a low dimensional …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by
providing temporal scopes (start and end times) on each edge in the KG. While Question …

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones.
Methods based on the relational path have shown strong, interpretable, and transferable …

Graph convolutional networks (GCNs)—which are effective in modeling graph structures—
have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC …