1. overview and survey from a logical / ILP perspective
Conclusions
overview and survey from a logical / ILP perspective
Distinction between
Model-based: BN, PLPs, PRMs,BLPs,...
vs. proof-based: SCFGs, SLPs, PRISM, ...
Learning Settings:
Learning from interpretations: PLPs,PRMs,BLPs
Learning from entailment: SLPs, PRISM
Learning from traces: RMMs, LOHMMs

2. Conclusions - continued
Learning includes principles from
Inductive logic learning / multi-relational data mining
Refinement operators
Background knowledge
Bias
Statistical learning
Likelihood
Independencies
Priors

3. Thank you for your ...
Sorry for all the probabilistic, logical stuff !
We hope that you have learned something !

4. Selected Links Conferences, Workshops & Summer Schools Systems & Data
AAAI-2000 workshop on "Learning Statistical Models from Relational Data" (SRL-2000)
Summer School on Relational Data Mining 2002
IJCAI-2003 workshop on "Learning Statistical Models from Relational Data" (SRL-2003)
ICML-2004 workshop on "Statistical Relational Learning and its Connections to Other Fields" (SRL-2004)
Forthcoming Dagstuhl seminar on "Probabilistic, Logical and Relational Learning - Towards a Synthesis"
Systems & Data
Probabilistic-Logical Model Repository
Projects
Evidence Extraction and Link Discouvery (EELD) DARPA Program
Efficient first-order probabilistic models for inference and learning, EPSRC research grant GR/N0739
Application of Probabilistic Inductive Logic Programming (APRIL I) European Union Assessment Project IST
Application of Probabilistic Inductive Logic Programming (APRIL II) Specific Targeted Research Project" funded by the European Commission under the "Sixth Framework Programme ( ); Information Society Technologies" "Future and Emerging Technologies" arm. Contract no. FP

5. Selected Publications

6. Selected Publications

7. Selected Publications

8. Selected Publications