J. Carme, R. Gilleron, A. Lemay, J. Niehren. INRIA FUTURS, University of Lille 3

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1 Interactive Learning o Node Selection Queries in Web Documents J. Carme, R. Gilleron, A. Lemay, J. Niehren INRIA FUTURS, University o Lille 3

2 Web Inormation Extraction Data organisation is : adapted to interace with human unadapted to automatic extraction speciic to each web site Example o task : Extraction tasks are uneasy extract s rom a web page

3 Queries or web documents A query : Input : HTML or XML Output : Parts o document Several possible representations : programs (perl,...) monadic queries by path expressions : XPath (W3C), modal logic PDL [Marx, PODS 2004] monadic queries in monadic Datalog [Gottlob & Koch PODS, LICS 2002] n-ary queries in MSO over trees [Thatcher & Wright 68] tree automata [Neven & Van de Bussche JACM 02, Neven & Schwentick TCS 02,...]

4 Conception o Queries Conception : Hand-made (Hard, Error-prone, need or expert) With visual tools like Lixto (Easier but still not easy) [Gottlob, Koch 2000 ;...] Automatically induced (Limited expressiveness and need a lot o examples) [Freitag, Kushmerick 2000 ; Kosala et al 2003 ; Muslea et al 2001 ;...] Solution? automatically induced using visual tools

5 Web documents as trees HTML HEAD BODY TABLE TABLE TABLE... TR TR TR TD TD TD... A A Trevor Bruss bruss@machin.edu Documents = Trees (no text and no attributes) Elements = Nodes Query a web page = select nodes on a tree Query = node selector

6 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

7 Plan 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

8 Demonstration o SQUIRREL Demonstration The SQUIRREL Algorithm integrated in Mozilla Fireox (available at carme/squirrel)

9 Plan 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

10 Learning rom completely annotated documents Input : Completely annotated documents (trees on Σ Bool) Output : A Node Selection Query q consistent with the input

11 Queries as tree languages A query : a tree language on (Σ Bool) b 0 b 0...

12 Queries as tree languages A query : a tree language on (Σ Bool) b 0 b 0... b b a

13 Queries as tree languages A query : a tree language on (Σ Bool) b 0 b 0... b matches with b 0 b a

14 Queries as tree languages A query : a tree language on (Σ Bool) b 0 b 0... b 0 matches with b 0

15 Queries as tree languages A query : a tree language on (Σ Bool) b 0 b 0... b 0 Tree languages representing queries are unctional : or each tree on T Σ, only one tree on T Σ Bool is in the language

16 Node Selecting Tree Transducers Deinition NSTT = unctional tree automata on Σ Bool

17 Node Selecting Tree Transducers Deinition NSTT = unctional tree automata on Σ Bool Properties Eicient : queries can be answered in polynomial time Expressive : as expressive as MSO queries Adapted to learning : Tree Automata can be learned using RPNI [Oncina, Garcia 93]

18 Inerence Algorithm or completely annotated examples RPNI [Oncina, Garcia 92, 93] Input : positive and negative (tree) examples Based on state merging methods Uses consistency checks Output : Tree automata

19 Inerence Algorithm or completely annotated examples RPNI [Oncina, Garcia 92, 93] Input : positive and negative (tree) examples Based on state merging methods Uses consistency checks Output : Tree automata RPNI NSTT [Carme, Gilleron, Lemay, Niehren 04] Input : (positive) completely annotated examples Based on state merging methods Uses unctionality tests Output : NSTT

20 Plan 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

21 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea b a 1 b a

22 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a b 0 a 0 The user provides partially annotated examples Complete annotation? : Errors!

23 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a b a 1 The user provides partially annotated examples Prune

24 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a The user provides partially annotated examples Prune and complete : Ok! We obtain a completely annotated pruned tree

25 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a a 1 How to prune? Too Strong : loss o expressibility Too Weak : need more annotations

26 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a How to prune? Too Strong : loss o expressibility Too Weak : need more annotations b 0 a 0

27 Partial annotations and pruning Target query : Extract a-leaves ater a b-lea = b a 1 b a a 1 How to prune? Too Strong : loss o expressibility Too Weak : need more annotations Our choice : keep the path and the position

28 pruning NSTT NSTT that prunes NSTT on (Σ Bool) { } language : a 1 a 1 a 1...

29 pruning NSTT NSTT that prunes NSTT on (Σ Bool) { } language : a 1 a 1 a 1... b a b a

30 pruning NSTT NSTT that prunes NSTT on (Σ Bool) { } language : a 1 a 1 a 1... matches with b a 1 b a a 1

31 pruning NSTT NSTT that prunes NSTT on (Σ Bool) { } language : a 1 a 1 a 1... matches with b a 1 b a 1 a 1

32 pruning NSTT NSTT that prunes NSTT on (Σ Bool) { } language : a 1 a 1 a 1...

33 Cut-unctionality ? - - Problem : several runs on dierent pruning may give dierent results on their common parts

34 Cut-unctionality ? - - Problem : several runs on dierent pruning may give dierent results on their common parts Cut-unctionality There is no inconsistency on annotation rom dierent pruning o the same tree.

35 Learning pruning NSTT pruning NSTT pruning NSTT = cut-unctional NSTT on pruned trees RPNI pnstt Input : partially annotated examples Based on state merging methods Uses cut-unctionality tests Output : pruning NSTT Cut-unctionality test can be done in polynomial time

36 Querying with pruning NSTT Querying with a pruning NSTT Can be done in polynomial time (equivalent to a run with a non deterministic automata)

37 Plan 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

38 O-line Experiments Tests on 26 benchmarks (adapted rom Kushmerick and Muslea) success partial success ailure Squirrel 22-4 WIEN (Kushmerick) Stalker (Muslea, Minton, Knoblock) Failure when the textual inormation is necessary or when pruning is too strong.

39 Interactive Learning # pages # annotations Okra-names Bigbook-addresses Yahoo E-bay NYTimes Google

40 Active Learning random Yahoo NYTimes Google active Yahoo NYTimes Google F-score with respect to the number o visited documents

41 Plan 1 Demo 2 Learning rom completely annotated documents 3 Learning rom partially annotated examples 4 Experiments 5 Conclusion

42 Conclusions Squirrel : outputs tree wrappers is based on grammatical inerence techniques (RPNI) is integrated in a visual interactive environment builds the wrapper through simple interaction perormances are good : good expressivity limited amount o interactions

43 Perspectives Ways o improvement Better pruning techniques Use o texts and attributes N-ary queries (multi-slot)

Towards Schema-Guided XML Query Induction

Towards Schema-Guided XML Query Induction Jérôme Champavère Rémi Gilleron Aurélien Lemay Joachim Niehren Université de Lille INRIA, France ICML-2007 Workshop on Challenges and Applications of Grammar Induction