In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of …

Apr 9, 2023 · These algorithms learn low-dimensional embeddings of entities and relations in a knowledge graph, allowing for efficient computation of similarity and inference tasks. In this …

Sep 21, 2023 · In this paper, we make a comprehensive overview of the current state of research in KG completion. In particular, we focus on two main branches of KG embedding (KGE) …

The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and evolution of science. …

In this tutorial we will explore how to use the knowledge embeddings generated by a graph of international football matches (since the 19th century) in clustering and classification tasks.

12 hours ago · Knowledge graph embedding (KGE) is an effective method for link prediction in knowledge graphs, with numerous models demonstrating significant success. However, many …

Sep 20, 2017 · 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 …

Oct 12, 2024 · Knowledge graph embedding aims to represent entities and relations in a Knowledge Graph (KG) as low-dimensional, dense vectors in a continuous feature space while …

Knowledge graph embeddings are data representation techniques used in knowledge graphs to convert the graph into a low-dimensional vector format.

Knowledge graph embeddings are designed to capture the semantic meaning and structure of the entities and relationships in the graph so that they can be easily manipulated and understood …