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Found 6 papers, 3 papers with code
What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?
Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context.
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.
Biomedical Knowledge Graph Embeddings with Negative Statements
Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction.
Benchmark datasets for biomedical knowledge graphs with negative statements
We also generate knowledge graph embeddings for each dataset with two popular path-based methods and evaluate the performance in each task.
Explainable Representations for Relation Prediction in Knowledge Graphs
We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs.
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
We investigate the impact of employing richer semantic representations that are based on more than one ontology, able to represent both genes and diseases and consider multiple kinds of relations within the ontologies.
