Simply logical: intelligent reasoning by example
Simply logical: intelligent reasoning by example
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Compressing probabilistic Prolog programs
Machine Learning
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
The Most Reliable Subgraph Problem
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Towards learning stochastic logic programs from proof-banks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Duce, an oracle-based approach to constructive induction
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Local query mining in a probabilistic prolog
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Network Simplification with Minimal Loss of Connectivity
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Fast discovery of reliable k-terminal subgraphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A framework for path-oriented network simplification
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Bisociative Knowledge Discovery
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Biomine and ProbLog are two frameworks to implement bisociative information networks (BisoNets). They combine structured data representations with probabilities expressing uncertainty. While Biomine is based on graphs, ProbLog's core language is that of the logic programming language Prolog. This chapter provides an overview of important concepts, terminology, and reasoning tasks addressed in the two systems. It does so in an informal way, focusing on intuition rather than on mathematical definitions. It aims at bridging the gap between network representations and logical ones.