2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Ivan Diliso, Nicola Fanizzi, Claudia d'Amato
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying…
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…
Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs
Christian Riefolo, Nicola Fanizzi, Claudia d'Amato
Tackling the problem of learning probabilistic classifiers from incomplete data in the context of Knowledge Graphs expressed in Description Logics, we describe an inductive approac…