241 citations · 263 across the 5 of their papers we have counts for
5 papers
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Tarek R. Besold, Artur d'Avila Garcez, Sebastian Bader +11
The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several…
A Probabilistic Approach to Knowledge Translation
Shangpu Jiang, Daniel Lowd, Dejing Dou
In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer…
Ontology Matching with Knowledge Rules
Shangpu Jiang, Daniel Lowd, Dejing Dou
Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two t…
The Libra Toolkit for Probabilistic Models
Daniel Lowd, Amirmohammad Rooshenas
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, an…
Closed-Form Learning of Markov Networks from Dependency Networks
Daniel Lowd
Markov networks (MNs) are a powerful way to compactly represent a joint probability distribution, but most MN structure learning methods are very slow, due to the high cost of eval…