Correlation embedding learning with dynamic semantic enhanced sampling for knowledge graph completion

TL;DR: This study introduces DRGCL, a disentangled relational graph neural network with contrastive learning, to acquire relation-aware entity representations for knowledge graph completion tasks, outperforming baseline models on three benchmark datasets.

TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.

TL;DR: MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.

TL;DR: The extraction of the DBpedia datasets is described, and how the resulting information is published on the Web for human-andmachine-consumption and how DBpedia could serve as a nucleus for an emerging Web of open data.

TL;DR: A novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted, and optimizes a carefully designed objective function that preserves both the local and global network structures.