This dissertation seeks to extend the scope and provide theoretical guidance for representation learning based query answering on knowledge graphs. The ...

This dissertation seeks to extend the scope and provide theoretical guidance for representation learning based query answering on knowledge graphs.

This dissertation seeks to extend the scope and provide theoretical guidance for representation learning based query answering on knowledge graphs. The ...

We introduce a relational graph neural network with bi-directional attention mecha- nism and hierarchical representation learning for open-domain question ...

Feb 22, 2024 · We propose a Federated Complex Query Answering framework (FedCQA), to reason over multi-source KGs avoiding sensitive raw data transmission to protect privacy.

Abstract. Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low- dimensional space.

We aim to learn feature representations of entities and relations in the knowledge graph. This is called representation learning.

In this paper, we study the out-of-sample repre- sentation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, ...

Apr 6, 2020 · Abstract:Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries.

This work investigates hyperbolic graph representation learning methods to effectively and efficiently represent knowledge base items and natural questions.