Applications of Inductive Logic Programming(ILP)

1. Applications of Inductive Logic Programming(ILP) 류정우

2. Table of Contents • Introduction • ILP Compared to Other Approaches to ML • Predicting the Mutagenicity of Chemical Compounds • Conclusion 1999-06-19

3. Introduction • ILP(Inductive Logic Programming) • Inductive machine learning + Logic programming • Components • B : Background knowledge • E+ : Positive Example • E- : Negative Example • H : Predicate logic formula, The Result • All Example in E+ can be derived from B  H • No Negative Example in E- can be derived from B  H 1999-06-19

4. QuickSort Example(1) • Examples(E+,E-) example(qsort( [], [a], [a] ), true). example(qsort( [a], [a], [a] ), false). example(qsort( [d,f,b,e,c,g,a], [a,b,c,d,e,f,g], [] ), true). example(qsort( [f,e,g], [e,f,g], [] ), true). example(qsort( [b,c,a], [a,b,c,d,e,f,g], [d,e,f,g] ), true). • Background Knowledge(B) partition( X, [], [], []). partition( X, [Y | Rest], [Y | Smalls], Bigs) :- gt(X,Y), !, partition(X, Rest, Smalls, Bigs). partition(X, [Y | Rest], Smalls, [Y | Bigs]) :- partition( X, Rest, Smalls, Bigs). 1999-06-19

5. QuickSort Example(2) • Induced Definition(H) • Using a ILP System, Markus qsort( [], L, L). qsort( [X | L], SL1A, SL2B) :- partition(L, X, L1, L2), qsort( L2, SL2A, SL2B), qsort( L1, SL1A, [X | SL2A] ). 1999-06-19

6. ILP Compared to Other Approaches to ML(1) • Attribute-based Learning • Decision tree,Neural network,Nearest neighbor • Advantages • Simple, Efficient, Effective for handling noise • Limits • Only for non-relational descriptions of objects • Limited form of Background knowledge • Induction almost has to start from scratch 1999-06-19

7. ILP Compared to Other Approaches to ML(2) • ILP • Advantage • General representation of Background Knowledge • EX) Learning about properties of chemical compounds • use molecular structure for background knowledge • Disadvantage • computational complexity 1999-06-19

8. Predicting the Mutagenicity of Chemical Compounds(1) • SAR(Structure/Activity) Relationship of chemical compounds • Traditional Way - Attribute-based • using global attributes of a molecule • EX) LUMO(Energy of the Lowest Molecular Orbital) • can’t use the patterns in the molecule structure 188 compounds successful Classical Regression 230 heteroaromatic nitro compounds These need ILP! 42 compounds failed 1999-06-19

9. Predicting the Mutagenicity of Chemical Compounds(2) • Using Progol - A ILP System Relationship atoms bonds charge Class Compound Compound High/Low mutagenicity • Background Knowledge • Classified Results of 230 Compounds • 18300 Prolog facts • LUMO(Energy of Lowest Unoccupied Molecular Orbital) • only for the 188 compounds amenable to regression 1999-06-19

10. Predicting the Mutagenicity of Chemical Compounds(3) • Result from Progol • For 188 Compounds • EX) Mutagenic if it has a RUMO value < -1.937 • 89% Accuracy(matching accuracy of regression) • but easy to comrehend and automatically generated • The Remaining 42 Compounds • 1 Rule with accuracy 88% • regression : 62% 1999-06-19

11. Predicting the Mutagenicity of Chemical Compounds(4) • Good Prediction Accuracy • New Chemical Insight Discovery of New Structural Feature! Five-membered aromatic ring with a nitrogen atom U Y = Z Mutagenicity V Double bond W X 1999-06-19

12. Conclusion • ILP의 적용 분야 Difficult, industrially or scientifically relevant NOT satisfactorily solved Relational Background > Attribute-value form Major Advantage Major Obstacle Background Knowledge in the form of General Program like Prolog Inefficiency - handling numerical data 1999-06-19