Conclusions

1. 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 • 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-2001-33035 • Application of Probabilistic Inductive Logic Programming (APRIL II) Specific Targeted Research Project" funded by the European Commission under the "Sixth Framework Programme (2002-2006); Information Society Technologies" "Future and Emerging Technologies" arm. Contract no. FP6-508861

5. Selected Publications

6. Selected Publications

7. Selected Publications

8. Selected Publications