2 citations · 2 across the 3 of their papers we have counts for
4 papers
Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs
Ivan Diliso, Roberto Barile, Claudia d'Amato +1
Datasets for the experimental evaluation of knowledge graph refinement algorithms typically contain only ground facts, retaining very limited schema level knowledge even when such…
GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs
Roberto Barile, Claudia d'Amato, Nicola Fanizzi
Since Knowledge Graphs are often incomplete, link prediction methods are adopted for predicting missing facts. Scalable embedding based solutions are mostly adopted for this purpos…
Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned
Claudia dAmato, Giuseppe Rubini, Francesco Didio +3
Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentenc…
Enhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models
Claudia d'Amato, Ivan Diliso, Nicola Fanizzi +1
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs. Embedding models are trained relying on bo…