Schema-Guided Query Induction
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1 Schema-Guided Query Induction Jérôme Champavère Ph.D. Defense September 10, 2010 Supervisors: Joachim Niehren and Rémi Gilleron Advisor: Aurélien Lemay
2 Introduction Big Picture XML: Standard language for storing and exchanging semi-structured data Widespread in database, Web and document communities Schemas: meta-description of XML documents XML queries: Select data in XML documents Basis for XML transformations J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
3 How to Design Queries? Introduction Experts manually define queries by: Writing Perl, Python or shell scripts Using W3C standards (XPath) Generating wrappers with tools like Lixto [Baumgartner et al., 2001] Average users need help: Generic approach No prior skills required Use of graphical user interfaces J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
4 Wrapper Induction for Web Information Extraction Introduction Automatic construction of wrappers (queries) Various machine learning techniques: String-based [Kushmerick, 2002] Tree-based: Inductive logic programming [Cohen et al., 2002] Tree automata inference [Kosala, 2003] Conditional random fields [Kristjansson et al., 2004] Classification [Marty et al., 2006] J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
5 Wrapper Induction for Web Information Extraction Introduction Automatic construction of wrappers (queries) Various machine learning techniques: String-based [Kushmerick, 2002] Tree-based: Inductive logic programming [Cohen et al., 2002] Tree automata inference Conditional random fields [Kristjansson et al., 2004] Classification [Marty et al., 2006] J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
6 Motivations for Schema-Guided Query Induction Introduction Add schema information into existing learning algorithms Learn queries consistent with the schema Define more accurate pruning strategies Formal characterization of heuristics Learnability results Classification of queries: what makes it hard to learn? Beyond HTML: XML data have a richer semantics thanks to schemas Nodes in XML trees associated to graphical objects J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
7 Contributions Introduction How to use schema knowledge in algorithms Infer schema-consistent queries Efficient inclusion checking New learnability result Schema-guided pruning Class of queries that are learnable w.r.t. pruning Formal characterization by stable queries New learnability result Relevance of stable queries in practice New experiments with XML datasets Best pruning strategies: schema-guided and related to stability J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
8 Contributions Introduction How to use schema knowledge in algorithms Infer schema-consistent queries Efficient inclusion checking New learnability result Schema-guided pruning Class of queries that are learnable w.r.t. pruning Formal characterization by stable queries New learnability result Relevance of stable queries in practice New experiments with XML datasets Best pruning strategies: schema-guided and related to stability J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
9 Outline Introduction 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
10 Outline XML, Schemas and Queries 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
11 XML XML, Schemas and Queries Example: representation of geographical data <country > <name>france< / name> < c i t y > Paris< / c i t y > <region > <name>nord Pas de Calais < / name> < p o p u l a t i o n > < / p o p u l a t i o n > < c i t y > L i l l e < / c i t y > < / region> <region > <name> V a l l ée du Rhône< / name> < c i t y >Lyon< / c i t y > < c i t y >Valence< / c i t y > < / region> < / country> J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
12 XML, Schemas and Queries Tree Representation Standard abstraction Example: country name city region region FR Paris name population city name city city NPDC Lille VdR Lyon Valence J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
13 XML, Schemas and Queries Tree Representation Standard abstraction Example: country name city region region FR Paris name population city name city city NPDC Lille VdR Lyon Valence We consider finite, ordered, unranked trees, over some alphabet Σ Text values (and attributes) are omitted J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
14 Schema Restrictions XML, Schemas and Queries Schemas are used to describe sets of valid trees Available in most XML applications Standard languages: DTD, W3C XML Schema, Relax NG,... Example of DTD over Σ: country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
15 Schema Restrictions XML, Schemas and Queries Schemas are used to describe sets of valid trees Available in most XML applications Standard languages: DTD, W3C XML Schema, Relax NG,... Example of DTD over Σ: country name city region region name (population + ɛ) city name ɛ city ɛ country population ɛ city region name population city city Invalid tree J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
16 Queries XML, Schemas and Queries We restrict ourselves to monadic node-selecting queries, i.e., functions that selects sets of nodes in trees: Q(t) nod(t). XPath: Standard language for node-selecting queries over XML trees Used by other XML standards Expressiveness: first-order logic over unranked trees [Marx, 2005] Example: //region[population]/name Richer formalisms: Monadic Datalog [Gottlob & Koch, 2004] Tree automata [Comon et al., 2007] Expressiveness: monadic second-order logic J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
17 Supervised Query Induction XML, Schemas and Queries What are the regions whose population is known? J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
18 Supervised Query Induction XML, Schemas and Queries Select one example region J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
19 Supervised Query Induction XML, Schemas and Queries Select one other example region J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
20 Supervised Query Induction XML, Schemas and Queries The system will automatically infer the query J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
21 Outline Tree Automata 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
22 Tree Automata Deterministic Tree Automata for Unranked Trees Finite state machines for dealing with sets of trees Models: Ranked tree automata over binary encodings of unranked trees Hedge automata [Brüggemann-Klein et al., 2001] Deterministic stepwise tree automata [Carme et al., 2004] Bottom-up deterministic tree automata over Curryfied encodings Best-suited for schema-guided query induction Can express schemas: non-trivial conversion J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
23 Deterministic DTDs Tree Automata country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ DTD D J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
24 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ name city region country qc 0 qc 1 qc 2 qc 3 Tree Automata region Glushkov automata: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
25 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ name city region country qc 0 qc 1 qc 2 qc 3 Tree Automata region region name pop. qr 0 qr 1 qr 2 city q 3 r city city Glushkov automata: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
26 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ name city region country qc 0 qc 1 qc 2 qc 3 Tree Automata region name q name region name pop. qr 0 qr 1 qr 2 city q 3 r city city Glushkov automata: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
27 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ name city region country qc 0 qc 1 qc 2 qc 3 Tree Automata region name q name city q city region name pop. qr 0 qr 1 qr 2 city q 3 r city city Glushkov automata: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
28 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ name city region country qc 0 qc 1 qc 2 qc 3 Tree Automata region name q name city q city region name pop. qr 0 qr 1 qr 2 city q 3 r city population q pop city Glushkov automata: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
29 Deterministic DTDs country name city region region name (population + ɛ) city name ɛ city ɛ population ɛ country qc 0 q name qc 1 q city qc 2 qr 1, qr 2, qr 3 qc 3 Tree Automata q 1 r, q 2 r, q 3 r name city q name q city q name region q 0 r q 1 r q 2 r q pop q city q city q 3 r population q pop q city Stepwise tree automaton: O( Σ D 2 ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
30 Deterministic DTDs Tree Automata country name city region region name (population + ɛ) city name ɛ city ɛ q reg population ɛ q country name q city q reg name city region population ǫ ǫ q name ǫ q name q city ǫ q reg ǫ q ǫ pop q city q city q pop q city ǫ q country ǫ Factorized tree automaton: O( Σ D ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
31 Tree Automata Efficient Inclusion Checking Theorem Let A be a tree automaton for unranked trees (stepwise, first-child next-sibling, or hedge), and D a deterministic DTD, both over Σ. Inclusion L(A) L(D) can be checked in time O( A Σ D ). Non-trivial algorithm: Avoid complement automaton computation Dectect inclusion failure on the pruduct automaton Only accessible part is materialized Optimizations: Early detection of inclusion failure Incremental w.r.t. adding ɛ-rules to A (stepwise) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
32 Monadic Queries Tree Automata Regular languages of annotated trees, i.e., over Σ {0, 1} Functional: no contradictory annotations Non-null: at least one positive annotation Node-selecting tree transducers (NSTTs) [Carme et al., 2007]: Stepwise tree automata for monadic node-selecting queries Functionality and non-nullity checked in polynomial time Query answering done in combined linear time J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
33 Tree Automata Regular Query Induction Supervised learning problem Based on tree automata inference (RPNI-like algorithm) Variant of Gold s identification in the limit model [Gold, 1978; de la Higuera, 1997] Regular monadic queries represented by NSTTs are indentifiable from completely annotated examples [Carme et al., 2007] J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
34 Outline Schema-Guided Pruning 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
35 Schema-Guided Pruning Why Pruning? Practical constraint: learn from partially annotated examples State-of-the-art systems use pruning in order to success [Carme et al., 2007; Raeymaekers, 2008] Basic idea: try to keep useful parts of tree examples that justify node selection, while removing irrelevant parts What can be learned depends on the fixed pruning strategy J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
36 Schema-Guided Pruning Pruning Strategy Replace whole subtrees by symbols in some alphabet Γ Function P from trees over Σ {0, 1} to trees over (Σ {0, 1}) Γ Preserve at least selected nodes and their path from the root Do not change label and annotation of preserved nodes Expressible by some monadic query Schema-guided: let Γ = sta(d) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
37 Path-Only Pruning Schema-Guided Pruning (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 0)(city, 0) (city, 0) Annotated tree t β J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
38 Path-Only Pruning Schema-Guided Pruning (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 0)(city, 0) (city, 0) Annotated tree t β J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
39 Path-Only Pruning Schema-Guided Pruning (country, 0) q name q city (region, 0) q reg country name ǫ qname qreg qreg ǫ ǫ qcountry (name, 1) q pop q city city region population ǫ qcity qname ǫ qpop ǫ qreg ǫ qpop qcity qcity ǫ qcity D-pruned tree Ponly D (t β) Schema D J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
40 Schema-Guided Pruning Path-Only Pruning (country, 0) (region, 0) (name, 1) U-pruned tree Ponly U (t β) country name city region population Schema U J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
41 Outline Stable Queries 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
42 Simple Queries? Stable Queries Select all names: country name city region region name population city name city city J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
43 Simple Queries? Stable Queries Select all regions names whose population is known: country name city region region name population city name city city J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
44 Simple Queries? Stable Queries Select country s name if all regions have no population: country name city region region name population city name city city J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
45 Simple Queries? Stable Queries Select country s name if all regions have no population: country name city region region name population city name city city Conclusion: the larger is the dependency, the more difficult it is. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
46 Insights Stable Queries (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 0) (city, 0) (city, 0) Annotated tree t Q(t) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
47 Insights Stable Queries (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 0) (city, 0) (city, 0) Relevant information for Q J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
48 Insights Stable Queries (country, 0) (region, 0) (name, 1) U-path-only pruning Ponly U (t Q(t)) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
49 Insights Stable Queries country city name region region name city city population name U-completion t J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
50 Insights Stable Queries (country, 0) (city, 0) (name, 0) (region, 0) (region, 0) (name, 0) (city, 0) (city, 0) (population, 0) (name, 1) Annotated tree t Q(t ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
51 Insights Stable Queries (country, 0) q name q city (region, 0) q reg (name, 1) q pop q city D-path-only pruning Ponly D (t Q(t)) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
52 Insights Stable Queries country name city region region name population city name D-completion t 1 J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
53 Insights Stable Queries (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 0) Annotated tree t 1 Q(t 1 ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
54 Insights Stable Queries country name city region region name population city name population city D-completion t 3 J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
55 Insights Stable Queries (country, 0) (name, 0) (city, 0) (region, 0) (region, 0) (name, 1) (population, 0)(city, 0)(name, 1)(population, 0)(city, 0) Annotated tree t 3 Q(t 3 ) J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
56 Stable Queries Stability Definition A query Q is stable by a D-pruning P (or P-stable) if and only if for all t 0 L(D), let t = P(t 0 Q(t 0 )): t 1 compl D (t ), ν ( t (ν) Σ {0, 1} ), ( ν Q(t 1 ) ν Q(t 0 ) ) Informally stated: for every D-completion of a D-pruned tree, a P-stable query Q selects exactly the same nodes on the non-pruned part as on the original tree. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
57 Stable Queries Classification of Pruning Strategies Definition Let P 1 be a D 1 -pruning and P 2 be a D 2 -pruning, P 1 is more preserving than P 2 if compl D1 (P 1 (s)) compl D2 (P 2 (s)) for all annotated trees s. D-path-only identity D-path-extended U-path-only more preserving U-path-extended Proposition Let Q be a query and P 1, P 2 be two pruning strategies. If P 1 is more preserving than P 2 and Q is P 2 -stable then Q is P 1 -stable. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
58 Outline Learnability Results 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
59 Tree Automata for Pruned Trees Learnability Results D-functionality: no contradictory annotations for D-pruned trees that share a D-completion Monadic queries: regular languages of D-pruned trees D-functional Non-null D-NSTTs D-functionality and non-nullity checked in polynomial time Query answering done in combined linear time J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
60 Learnability Results Stability versus D-functionality Lemma Let Q be a monadic query, P be a D-pruning, and L the language of D-pruned trees such that L = {P(t Q(t)) t L(D)}. If Q is P-stable then L is D-functional. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
61 Learning Algorithm Learnability Results trpni P D(S) / / parameters : D, a schema ; P, a D pruning s t r a t e g y / / i n p u t : S, a sample of D, P pruned t r e e s / / output : A, a D NSTT A init(s) / / maximal d e t e r m i n i s t i c automaton recognizing S while (p 1, p 2 ) pick_two_states(a) do / / s t a t e s p r o p e r l y ordered A deterministically_merge(a, p 1, p 2 ) i f A i s a D NSTT then / / check D f u n c t i o n a l, non n u l l A A return A J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
62 Main Result Learnability Results Theorem Let D be a deterministic schema over Σ and P a D-pruning strategy. The class of P-stable queries represented by deterministic D-NSTTs over Σ is learnable from D-pruned examples by trpni P D in the following sense: 1 given a sample S of D, P-pruned trees, trpni P D (S) returns a D-NSTTs in time polynomial in S ; 2 for all D-NSTTs A representing a P-stable query, there exists a characteristic set CS of D, P-pruned trees of cardinality polynomial in A for which, given S CS consistent with A, trpni P D (S) returns a D-NSTTs equivalent to A. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
63 Learnability Results Proof Sketch Reduction onto learnability of regular monadic queries (trpni) Based on the relation between P-stable queries and D-functionality Characteristic set obtained by D-completion Bisimulation of trpni and trpni P D J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
64 Learnability Results Learnability of Schema-Consistent Queries Corollary Let D be a deterministic schema. The class of D-consistent queries represented by NSTTs can be learned from completely annotated examples. Proof: trpni P D with P = P id and D-inclusion checking. J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
65 Outline Experiments 1 XML, Schemas and Queries 2 Tree Automata 3 Schema-Guided Pruning 4 Stable Queries 5 Learnability Results 6 Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
66 Experiments Some Practical Questions XML data rather than HTML data Does schema s semantics help? Are stable queries relevant? Given a query that is stable by several pruning strategies, which one is the most approriate? J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
67 Experiments Settings New learning system in OCaml Learning algorithm implemented parameters: Prunings: P U only, PU ext., PD only, PD ext. Dynamic/static inclusion checking, horizontal typing heuristic Several combinations are not theoretically complete J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
68 Experiments Protocol Simulation of the user behaviour when defining a new query Interactions between the user and the system: The user provides annotated examples to the system The system infers a new query, which in turn the user evaluates More examples while not satisfied Weak interactions: Annotations per document instead of per node Sufficient for evaluating the system Cross-validation by repeated random sub-sampling Compute F-measure w.r.t. number of examples J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
69 Experiments XPath Queries XMark-A1: /site/closed_auctions/closed_auction /annotation/description/text/keyword XMark-02: /site/open_auctions/open_auction/bidder[1]/increase XMark-21: /site/open_auctions/open_auction[count(bidder) 3]/itemref XMark-A6: /site/people/person[profile/gender and profile/age]/name J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
70 Datasets Experiments Query id. # doc. Ponly U Stable w.r.t. Pext. U Ponly D Pext. D XMark-A1 50 yes yes yes yes XMark no yes yes yes XMark no yes yes yes XMark-A6 250 no no no yes J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
71 Pruning Alone Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
72 Combination of Heuristics Experiments J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
73 Experiments Facts Confirmation: pruning is essential for XML query induction Contextual information brought by the schema are very useful The most appropriate pruning is one for which the query is stable, and which makes use of the schema Combining dynamic inclusion checking with schema-guided pruning enables to learn difficult queries J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
74 Conclusion Summary Main contributions Complete learning algorithms Stable queries Schema-consistent queries Classification of pruning strategies w.r.t. stability Experiments on XML datasets Efficient inclusion checking (Information and Computation, 2009) Both practical and theoretical interest Factorized tree automata for DTDs J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
75 Conclusion Summary Main contributions Complete learning algorithms Stable queries Schema-consistent queries Classification of pruning strategies w.r.t. stability Experiments on XML datasets Efficient inclusion checking (Information and Computation, 2009) Both practical and theoretical interest Factorized tree automata for DTDs Some lessons we can drawn... Using schema information improves XML query induction Theoretical fundations Practical justification Difficulty scale of target queries can be addressed through stability J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
76 Conclusion Perspectives How perform other learning approches on stable queries? Interactive learning: Start with the less preserving pruning (i.e., P D only ) Evolve towards more preserving prunings n-ary queries: How should we prune? Same definition of stable queries? Text values: schemas may contain type information Extension to XML transformations: notion of stability? J. Champavère (Université Lille 1 LIFL Inria Mostrare) Schema-Guided Query Induction September 10, /46
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