Convolutional complex knowledge graph embeddings
We investigate the problem of learning continuous vector representations of knowledge
graphs for predicting missing links. Recent results suggest that using a Hermitian inner …
graphs for predicting missing links. Recent results suggest that using a Hermitian inner …
Constructing negative samples via entity prediction for multi-task knowledge representation learning
G Chen, J Wu, W Luo, J Ding - Knowledge-Based Systems, 2023 - Elsevier
Abstract Knowledge representation learning (KRL) aims at embedding the entities and
relations in knowledge graphs (KGs) into vectors by learning. In recent years, several multi …
relations in knowledge graphs (KGs) into vectors by learning. In recent years, several multi …
Knowledge Graph Completion Using Structural and Textual Embeddings
SK Alqaaidi, KJ Kochut - IFIP International Conference on Artificial …, 2024 - Springer
Abstract Knowledge Graphs (KGs) are widely employed in artificial intelligence applications,
such as question-answering and recommendation systems. However, KGs are frequently …
such as question-answering and recommendation systems. However, KGs are frequently …
DRILL-- Deep Reinforcement Learning for Refinement Operators in
Approaches based on refinement operators have been successfully applied to class
expression learning on RDF knowledge graphs. These approaches often need to explore a …
expression learning on RDF knowledge graphs. These approaches often need to explore a …
[HTML][HTML] Hardware-agnostic computation for large-scale knowledge graph embeddings
Abstract Knowledge graph embedding research has mainly focused on learning continuous
representations of knowledge graphs towards the link prediction problem. Recently …
representations of knowledge graphs towards the link prediction problem. Recently …
ShallowBKGC: a BERT-enhanced shallow neural network model for knowledge graph completion
N Jia, C Yao - PeerJ Computer Science, 2024 - peerj.com
Abstract Knowledge graph completion aims to predict missing relations between entities in a
knowledge graph. One of the effective ways for knowledge graph completion is knowledge …
knowledge graph. One of the effective ways for knowledge graph completion is knowledge …
Relations Prediction for Knowledge Graph Completion using Large Language Models
SK Alqaaidi, K Kochut - arXiv preprint arXiv:2405.02738, 2024 - arxiv.org
Knowledge Graphs have been widely used to represent facts in a structured format. Due to
their large scale applications, knowledge graphs suffer from being incomplete. The relation …
their large scale applications, knowledge graphs suffer from being incomplete. The relation …
Out-of-vocabulary entities in link prediction
Knowledge graph embedding techniques are key to making knowledge graphs amenable to
the plethora of machine learning approaches based on vector representations. Link …
the plethora of machine learning approaches based on vector representations. Link …
ASLEEP: a shallow neural model for knowledge graph completion
N Jia - International Conference on Neural Information …, 2022 - Springer
Abstract Knowledge graph completion aims to predict missing relations between entities in a
knowledge graph. One of the effective ways for knowledge graph completion is knowledge …
knowledge graph. One of the effective ways for knowledge graph completion is knowledge …
From Text to Triples: NLP-Driven Approaches for Knowledge Graph Construction and Completion
SK Alqaaidi - 2024 - search.proquest.com
Comprehending knowledge is a crucial focus in Artificial Intelligence (AI). A knowledge
graph is a structured repository for entities and their relationships, organized in a graph …
graph is a structured repository for entities and their relationships, organized in a graph …