state of the rta applications the infomix project infomix ...polleres/teaching/lma2006/unit3.pdfa...

State of the artApplications

The INFOMIX ProjectINFOMIX Live Demo

Answer Set Programming for the Semantic Web

Tutorial

Thomas Eiter, Roman Schindlauer (TU Wien)Giovambattista Ianni (TU Wien, Univ. della Calabria)

Axel Polleres (Univ. Rey Juan Carlos, Madrid)

Supported by IST REWERSE, FWF Project P17212-N04, CICyT project TIC-2003-9001-C02.

G. Ianni Unit 3 � ASP: State of the Art and Applications

Unit 3 � ASP: State of the Art and Applications

G. Ianni

Dipartimento di Matematica - Università della Calabria

European Semantic Web Conference 2006

Unit Outline

1 State of the art

2 Applications

3 The INFOMIX Project

4 INFOMIX Live Demo

A very active �eld

Major Scienti�c Events have ASP as hot topic

• Intl. Workshop on ASP ('01, '03 and '05)

• LPNMR, NMR, JELIA

• Special Issue on Answer Set Programming (ASP) in AMAI

• Working group on Answer Set Programming (WASP, 15+nodes)

Mature Solvers

• DLV [35], Smodels [68]

• ASSAT, Cmodels, dcs, DeRes, DisLog, DisLop, NoMoRe,aspps, SLG

A very active �eld

Major Scienti�c Events have ASP as hot topic

• Intl. Workshop on ASP ('01, '03 and '05)

• LPNMR, NMR, JELIA

• Special Issue on Answer Set Programming (ASP) in AMAI

• Working group on Answer Set Programming (WASP, 15+nodes)

Mature Solvers

• DLV [35], Smodels [68]

• ASSAT, Cmodels, dcs, DeRes, DisLog, DisLop, NoMoRe,aspps, SLG

ASP Points of strength

Totally declarative

Order of rules and atoms do not matter. You can ignore how the solveroperates

Decidable

Prototypes started from Datalog without function symbols. Extensions keepdecidability.

Monotonic and nonmonotonic

Negation as failure, as well as classic (�with strong semantics�) negation

Nondeterministic

You can specify a set of possible worlds (�guesses�) you want. Dealing withuncertainty is thus enabled.

Totally declarative

Decidable

Nondeterministic

Totally declarative

Decidable

Nondeterministic

Totally declarative

Decidable

Nondeterministic

ASP Points of strength - 2

Versatile

Weak and Soft constraints, useful special constructs with well-de�ned formalsemantics

Scalable

Can compete with top-down solvers now

Interoperable

• External built-ins, External predicates

• DLV Java API and ODBC Interface

• RuleML schema for program exchange

Note that the price of each achievement in terms of research work is high in

the context of ASP (full declarativity is a big design constraint), but it pays o�

Versatile

Scalable

Interoperable

Versatile

Scalable

Interoperable

Versatile

Scalable

Interoperable

Current state-of-the-art

Semantics

• Introduction of Function Symbols [71, 14, 7]

• Introduction of various forms of Generalized Quanti�ers (e.g.Aggregates [32, 55, 63, 26])

• Study of equivalence [25], and debuggers [30, 8]

Scalability

• Intelligent grounders, magic sets [51, 18]

• New heuristics for model generation [33, 31]

• Parallel execution [38]

• Intelligent reductions to SAT [37]

Applications

Hot Areas (non-complete list)

• Con�guration/composition,

• Information integration,

• Security analysis,

• Agent systems,

• Semantic Web (see Units 4�6),

• Planning.

ASP is very well tailored at modelling problems that �ts the G-C-Oapproach and need fast prototyping. In an increasing number ofcases, ASP technologies can be kept in release versions of softwaresthey are embedded in.

Applications

• Agent systems,

• Planning.

Applications

• Agent systems,

• Planning.

Applications

• Agent systems,

• Planning.

A Web Service Composition problem

Legenda

• Frame = Web Service

• Boxed White Frame = Final Goal

• a→ b = Output of a ful�lls input preconditions for b

A Web Service Composition problem - 2

Some assumption

• Arrows are statically given.

• But they can come from any chosen semantic entailment.

• Also conjunctive conditions are possible (not shown).

A Web Service Composition problem - 3

ASP role

• To design whatever strategy for execution plan generation.

• One can use Guess, Check, and Optimize methodology.

• [64] won the EEE-Web'05 WS contest.

Data Integration Systems

• O�er uniform access to a set of heterogeneous sources

• The representation provided to the user is called global schema

• The user is freed from the knowledge about data location andformat

When the user issues a query overthe global schema, the system:

• determines which sources toquery and how

• issues suitable queries to thesources

• assembles the results andprovides the answer

ApplicationApplication

Global SchemaGlobal Schema

The INFOMIX architecture

DesignerUser

InformationService Level

InternalIntegration Level

Data Acquisition andTransformation Level

Data Sources

DB HTMLXML

. . ...............................

..........

INFOMIX

Query Formulation

Query Rewriter Query OptimizerComputationalLogic System

Information Model Manager

Data Acquisition and Transformation

InternalData Store

Three Layers:

DesignerUser

Data Sources

DB HTMLXML

. . ...............................

..........

INFOMIX

Query Formulation

InternalData Store

Three Layers:

Extraction:

DesignerUser

Data Sources

DB HTMLXML

. . ...............................

..........

INFOMIX

Query Formulation

InternalData Store

Three Layers:

Processing:

Internal Integration Level

DesignerUser

Data Sources

DB HTMLXML

. . ...............................

..........

INFOMIX

Query Formulation

InternalData Store

Three Layers:

Frontend:

Information Service Level

DesignerUser

Data Sources

DB HTMLXML

. . ...............................

..........

INFOMIX

Query Formulation

InternalData Store

Design Time

A designer speci�es sources and mappings from sources to theglobal schema

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

GAV = Global as view

Design Time

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

Design Time

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

Design Time - 2

The designer can specify also constraints on the global schema, e.g.KeyConstraint(g,1)

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

4 5Con�ict!!

Design Time - 2

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

4 5Con�ict!!

Design Time - 2

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

4 5Con�ict!!

Design Time - 2

Data Sources

⇒ GAV Mapping

g(X,Y) :- ps1(X,Y).

g(X,Y) :- ps2(X,Y).

⇒Global Schema

4 5Con�ict!!

Run Time

When a query is submitted, this has to be unfolded to the sourcesand a merging program has to be processed. But query answeringunder constraints is a NP-hard problem also in the simpler settings.

Two possible repairs

⇒De�nite answers(cautious reasoning)

Idea: The query answering and con�ict repair strategy can beprogrammed with ASP

Run Time

How ASP comes into play

Bad News:

• Sources contain a huge amount of data

• Evaluating a co-NP hard problem is unfeasible

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

A brute force approach would consider k rules and n = 2k minimal models

Ground program

p(1) v q(1) :- a(1).

p(k) v q(k) :- a(k).

⇒Stable Models

M1 : {p(1), p(2), . . . , p(k − 1), p(k)} ∪ DM2 : {p(1), p(2), . . . , p(k − 1), q(k)} ∪ D. . .Mn : {q(1), q(2), . . . , q(k − 1), q(k)} ∪ D

Bad News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

p(k) v q(k) :- a(k).

⇒Stable Models

Bad News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

p(k) v q(k) :- a(k).

⇒Stable Models

Bad News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

p(k) v q(k) :- a(k).

⇒Stable Models

Magic Sets

Program P ,Query Q

⇒ Magic Sets

Program rewriting⇒ Rewritten Program

MS(Q,P)

Magic sets:

• focus on the subset of P which is relevant for Q

• push down the query constants, to eliminate rule instances which cannotcontribute to the derivation of Q

• simulate the top-down evaluation of Q

Applicability:

• Positive Programs (in the literature)

• Disjunctive programs (INFOMIX achievement)

• Programs with un-strati�ed negation (INFOMIX achievement)

Magic Sets

Program P ,Query Q

⇒ Magic Sets

MS(Q,P)

Magic sets:

Applicability:

Magic Sets

Program P ,Query Q

⇒ Magic Sets

MS(Q,P)

Magic sets:

Applicability:

Magic Sets

Program P ,Query Q

⇒ Magic Sets

MS(Q,P)

Magic sets:

Applicability:

Magic Sets

Program P ,Query Q

⇒ Magic Sets

MS(Q,P)

Magic sets:

Applicability:

Magic Sets

Program P ,Query Q

⇒ Magic Sets

MS(Q,P)

Magic sets:

Applicability:

Magic Sets approach has been extended to the ASP

Good News:

• Not all the data is necessary for answering user queries

• Magic sets can focus on relevant data

• Problems theoretically untractable become feasible

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

An intelligent approach could consider only one ground rule and 2 models

Ground program

p(1) v q(1) :- a(1).

⇒ Stable Models

M1 : {p(1)} ∪ DM2 : {q(1)} ∪ D

Good News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

⇒ Stable Models

M1 : {p(1)} ∪ DM2 : {q(1)} ∪ D

Good News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

⇒ Stable Models

M1 : {p(1)} ∪ DM2 : {q(1)} ∪ D

Good News:

A simple program

p(X) v q(X) :- a(X).

Database and Query

Database:D = { a(1),a(2), ...,a(k) }

Query: p(1)?

Ground program

p(1) v q(1) :- a(1).

⇒ Stable Models

M1 : {p(1)} ∪ DM2 : {q(1)} ∪ D

The lesson of INFOMIX

• The INFOMIX prototype is the most expressive current systemfor consistent query answering under incompleteness

• Expresses the full range of queries (not only fragments), withdi�erent sorts of constraints (KDs, IDs, EDs)

• Rich Data Acquisition and Transformation Layer

• Fruitful use of computational logic (proof of concept)

• Experimental results are encouraging, scalability feasible

• Further e�orts for optimizing data access (cf. constantpushing)

• Tighter coupling between CL system and relational engine

Live Demo

Now, let's play with the Live Demo

http://www.mat.unical.it/infomix

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