Search Results for author:
Found 3 papers, 2 papers with code
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs.
Ranked #2 on
Link Property Prediction
on ogbl-biokg
Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter
Informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients.
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs
We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms.
Ranked #7 on
Link Prediction
on FB15k
