Knowledge graph embeddings in the biomedical domain: Are they useful? a look at link prediction, rule learning, and downstream polypharmacy tasks
Knowledge graphs (KGs) are powerful tools for representing and organizing complex
biomedical data. They empower researchers, physicians, and scientists by facilitating rapid …
biomedical data. They empower researchers, physicians, and scientists by facilitating rapid …
Efficient memory-enhanced transformer for long-document summarization in low-resource regimes
Long document summarization poses obstacles to current generative transformer-based
models because of the broad context to process and understand. Indeed, detecting long …
models because of the broad context to process and understand. Indeed, detecting long …
[HTML][HTML] Knowledge graphs in pharmacovigilance: a step-by-step guide
M Hauben, M Rafi - Clinical Therapeutics, 2024 - Elsevier
Purpose This work aims to demystify Knowledge Graphs (KGs) in pharmacovigilance (PV). It
complements the scoping review within this issue. By bridging knowledge gaps and …
complements the scoping review within this issue. By bridging knowledge gaps and …
Sem@ K: Is my knowledge graph embedding model semantic-aware?
Using knowledge graph embedding models (KGEMs) is a popular approach for predicting
links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction …
links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction …
Enhancing Dissolved Oxygen Concentrations Prediction in Water Bodies: A Temporal Transformer Approach with Multi-Site Meteorological Data Graph Embedding
H Wang, L Zhang, R Wu, H Zhao - Water, 2023 - mdpi.com
Water ecosystems are highly sensitive to environmental conditions, including meteorological
factors, which influence dissolved oxygen (DO) concentrations, a critical indicator of water …
factors, which influence dissolved oxygen (DO) concentrations, a critical indicator of water …
JKRL: Joint knowledge representation learning of text description and knowledge graph
G Xu, Q Zhang, D Yu, S Lu, Y Lu - Symmetry, 2023 - mdpi.com
The purpose of knowledge representation learning is to learn the vector representation of
research objects projected by a matrix in low-dimensional vector space and explore the …
research objects projected by a matrix in low-dimensional vector space and explore the …
An Evaluation of Link Prediction Approaches in Few-Shot Scenarios
Semantic models are utilized to add context information to datasets and make data
accessible and understandable in applications such as dataspaces. Since the creation of …
accessible and understandable in applications such as dataspaces. Since the creation of …
Large-scale knowledge graph representation learning
M Badrouni, C Katar, W Inoubli - Knowledge and Information Systems, 2024 - Springer
The knowledge graph emerges as powerful data structures that provide a deep
representation and understanding of the knowledge presented in networks. In the pursuit of …
representation and understanding of the knowledge presented in networks. In the pursuit of …
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic
meanings. KGs are being used in a wide range of applications. However, they inherently …
meanings. KGs are being used in a wide range of applications. However, they inherently …
Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs
K Tatchukova, Y Qu - IEEE Access, 2024 - ieeexplore.ieee.org
Knowledge Graphs have demonstrated a real advantage in knowledge representation,
leveraging graphs NoSQL structures and schema-less technology, which offers superior …
leveraging graphs NoSQL structures and schema-less technology, which offers superior …