1 citations · 1 across the 4 of their papers we have counts for
5 papers
ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction
Alberto Bernardi, Luca Costabello, Christophe Gueret
Knowledge Graph Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missing links. However, the qualit…
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello +4
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by di…
Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
Alberto Bernardi, Luca Costabello
Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Kn…
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning
Jeffrey Sardina, Luca Costabello, Christophe Guéret
Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in…
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
Vasileios Baltatzis, Luca Costabello
Despite being the go-to choice for link prediction on knowledge graphs, research on interpretability of knowledge graph embeddings (KGE) has been relatively unexplored. We present…