Remarkable progress has been made on automated reasoning with knowledge
...
Recent years have witnessed much interest in temporal reasoning over
kno...
Representative selection (RS) is the problem of finding a small subset o...
Graph neural networks (GNNs) work well when the graph structure is provi...
Many important problems can be formulated as reasoning in multi-relation...
Time is an important feature in many applications involving events that ...
Knowledge graphs (KGs) typically contain temporal facts indicating
relat...
Graphs arise naturally in many real-world applications including social
...
We consider the problem of learning Relational Logistic Regression (RLR)...
Consider the following problem: given a database of records indexed by n...
The aim of knowledge graphs is to gather knowledge about the world and
p...
Statistical relational AI (StarAI) aims at reasoning and learning in noi...
Relational probabilistic models have the challenge of aggregation, where...
In recent work, we proved that the domain recursion inference rule makes...
Statistical relational models provide compact encodings of probabilistic...
Relational logistic regression (RLR) is a representation of conditional
...
First-order knowledge compilation techniques have proven efficient for l...