OWL-Gres vs Quonto OWL-Gres vs Quonto Angela Alvarez Rubio Angela Alvarez Rubio

OWL-Gres OWL-Gres vs vs

QuontoQuonto Angela Alvarez RubioAngela Alvarez Rubio

IntroductionIntroduction Using ontologies as a conceptual point of view on Using ontologies as a conceptual point of view on

repositories of data is increasinglyrepositories of data is increasingly

These ontologies deal with large amounts of dataThese ontologies deal with large amounts of data Most important parameter on computational Most important parameter on computational

complexity of reasoningcomplexity of reasoning

We will want a polynomial reasoning!We will want a polynomial reasoning!

Data sizeData size

And we want can do complex questionsAnd we want can do complex questions

IntroductionIntroduction

1. Perfect reformulation:1. Perfect reformulation: taking into account the TBOX taking into account the TBOX T, the q query is reformulated in a new queryT, the q query is reformulated in a new query

2 stapes: 2 stapes:

On a DL-LiteOn a DL-Lite

Conjunctive query is a union of conjunctive wich size Conjunctive query is a union of conjunctive wich size does not depend on Adoes not depend on A

We can evaluate it with LOGSPACE on We can evaluate it with LOGSPACE on the ABOX size the ABOX size

IntroductionIntroduction

2. Query Evaluation:2. Query Evaluation: the new query is evaluated only the new query is evaluated only in the ABox to produce the answer in the ABox to produce the answer

2 stapes: 2 stapes:

The evaluation of the query can be delegated to an engine The evaluation of the query can be delegated to an engine SQL database with optimization of querys strategiesSQL database with optimization of querys strategies

The ABox, is maintained through a RDBMS (Data The ABox, is maintained through a RDBMS (Data management systems relational) in the secondary management systems relational) in the secondary storage to control a large data numberstorage to control a large data number

Because is the unique tecnologyBecause is the unique tecnology

IntroductionIntroduction We presented two systems to work We presented two systems to work

with large amounts of data: with large amounts of data:

OWL-GresOWL-Gres

QuontoQuonto

IntroductionIntroduction TargetsTargets

Compare the OWL-Gres system with Compare the OWL-Gres system with Quonto systemQuonto system

Discover the DL-Lite fragment in which Discover the DL-Lite fragment in which is based in OWL_Gres is based in OWL_Gres

QuontoQuonto Is a tool that implements the DL-Lite Is a tool that implements the DL-Lite

query answering algorithmquery answering algorithm

Their limitations will depend of the single Their limitations will depend of the single engine DBMengine DBM

Is capable of answering questions Is capable of answering questions about ABOXes wich containing about ABOXes wich containing millions of assertionsmillions of assertions

Delegates to a RBDMS the storing of the Delegates to a RBDMS the storing of the ABOXABOX

Quonto: Quonto: DL-Lite A+DL-Lite A+

Represents the domain in terms of concepts, sets of Represents the domain in terms of concepts, sets of objects, and roles and permets: objects, and roles and permets:

Is the fragment DL-Lite largest known in order to obtain Is the fragment DL-Lite largest known in order to obtain LOGSPACE data complexityLOGSPACE data complexity

Value-Domains: domains that denote specific sets of values (data)Value-Domains: domains that denote specific sets of values (data)

Enjoys FOL-rewritabilityEnjoys FOL-rewritability

Concept attributes: binary relations between objects and valuesConcept attributes: binary relations between objects and values

Role attributes: ternary relations between pairs of objects and valueRole attributes: ternary relations between pairs of objects and value

Allows for functionality assertions and role inclusion Allows for functionality assertions and role inclusion assertions, but with some restrictions:assertions, but with some restrictions: No functional role or attribute can be specialized by using it in No functional role or attribute can be specialized by using it in

the right-hand side of a role or attribute inclusion assertionsthe right-hand side of a role or attribute inclusion assertions

Quonto: Quonto: DL-Lite A+DL-Lite A+

The knowledge The knowledge base (KB) is base (KB) is formed by: formed by:

K=<T, A> K=<T, A>

T: TBOXT: TBOX to to represent intensional represent intensional knowledgeknowledge

A: ABOXA: ABOX to represent to represent extensional extensional knowledgeknowledge

Member AssertionsMember Assertions

Concept inclusion assertion: B ⊑ CConcept inclusion assertion: B ⊑ C

Role functionality assertion: funct Q Role functionality assertion: funct Q

Role inclusion assertion: Q ⊑ R Role inclusion assertion: Q ⊑ R

Value-domain inclusion assertion: Value-domain inclusion assertion: E ⊑ F E ⊑ F

Attribute inclusion assertion: U ⊑ V Attribute inclusion assertion: U ⊑ V

Attribute functionality assertion: Attribute functionality assertion: funct U funct U

A(c), P(c; c0), A(c), P(c; c0),

UUCC(c; d) U(c; d) URR(a, b, c)(a, b, c)

Attribute Role: funct R Attribute Role: funct R

Quonto:Quonto: Query answeringQuery answering

x: Distinguished variablesx: Distinguished variables y: Non-distinguished variables y: Non-distinguished variables conj (x, y): atoms:conj (x, y): atoms:

A(xA(xoo)) P(xP(xoo, y, yoo)) D(xD(xvv) ) UUCC(x(xoo,, xxvv)) UURR(x(xoo,y,y00 x xvv))

Query conjunctive in a KB K:Query conjunctive in a KB K:

q(x) ←q(x) ←y. conj(x,y)y. conj(x,y) xxoo, y, yoo are variables in x and y or are variables in x and y or

constants in Гconstants in ГOO xxvv is a variable in x and y a constant is a variable in x and y a constant

in in ГГVV

Union of conjunctive queries (UCQ):Union of conjunctive queries (UCQ):

Certain answers all tuples t of elements of Certain answers all tuples t of elements of ГГVV Г ГOO such that, when substituted to x in such that, when substituted to x in

q(x), we have that K |= q(t)q(x), we have that K |= q(t)q(x) ←Vq(x) ←Viiyyii. conj(x,y). conj(x,y)

Firs TargetFirs Target On what DL-Lite fragment On what DL-Lite fragment

is based OWL-Gres? is based OWL-Gres?

2 step

s:

2 step

s: See the characteristics of potential See the characteristics of potential

fragments and differentiate itfragments and differentiate it

See if OWL-Gres accept this characteristicsSee if OWL-Gres accept this characteristics

Java ProgramJava Program

Protege toolProtege tool

First Target: First Target: FragmentsFragmentsConstructor Sintax Example

Atomic conc A Doctor

Exist. Restr Q child-

At. conc. neg. A Doctor

Conc. neg. Q child

Atomic role P Child

Inverse role P- child-

Role negation Q manages

Conc. incl. Cl ⊑ Cr Father ⊑ child

Mem. asser. A(c) Father(bob)

First Target: First Target: FragmentsFragments

Constructor Sintax Example

Atomic conc A Doctor

Exist. Restr Q child-

At. conc. neg. A Doctor

Conc. neg. Q child

Atomic role P Child

Inverse role P- child-

Role negation Q manages

Conc. incl. Cl ⊑ Cr Father ⊑ child

Mem. asser. A(c) Father(bob)


Mem. asser. P (c1,c2) Child(bob, ann)

Role incl. Q ⊑ R hasFather ⊑ child-

Disjointness between roles Q ⊑ Q child ⊑ child

Funct. asser. funct (Q) (funct father)

Top conc ТC

Qualified exist. restriction Q.C child.Male

Attribute domain (U) (salary)

Top domain ТD

Datatype Ti xsd: int

Attribute range (U) (salary)






Top conc ТC



Top domain ТD








Top conc ТC



Top domain ТD








Top conc ТC



Top domain ТD




Atomic attribute U Salary

Attribute negation U salary

Object constant C John

Value constant V ‘john’

Mem. asser. U (c,d) phone(bob, ‘2345’)

Atr.funct asser. funct (U) (funct ssn)

Role Incl. asser. Q ⊑ R Father ⊑ anc

V.Dom Incl. asser. E ⊑ F (age) ⊑ xsd: int

Atri. Incl asser. U ⊑ V offPhone ⊑ phone

First Target: First Target: SearchSearch We use a TBOX based We use a TBOX based in the university in the university

hierarchy: hierarchy:

First Target: First Target: SearchSearch Initially our TBOX is compatible with Initially our TBOX is compatible with

OWL-Gres: OWL-Gres:

C:\Documents and Settings\Propietario\workspace\OwlGres21-jul-2008 12:54:42 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Total number of triples: 61721-jul-2008 12:54:42 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owlThe TBox is compatible with DL-Lite

First Target: First Target: SearchSearch We verify for DL-Lite F: We verify for DL-Lite F:

First Target: First Target: SearchSearch We verify for DL-Lite F: We verify for DL-Lite F:

C:\Documents and Settings\Propietario\workspace\OwlGres21-jul-2008 12:57:25 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Total number of triples: 61821-jul-2008 12:57:25 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owlFRAGMENT ERROR: No support for axiom OWLFunctionalObjectPropertyAxiom

On OWL Axiom: FunctionalObjectProperty(takesCourse)

The TBox is not compatible with DL-Lite

The TBos is The TBos is not not

compatible compatible with with

DL- DL-Lite FR Lite FR or DL-Lite Aor DL-Lite A

DL-Lite DL-Lite RR

DL-Lite FDL-Lite FDL-Lite DL-Lite FRFR

DL-Lite DL-Lite AA

First Target: First Target: SearchSearch We verify for DL-Lite R: We verify for DL-Lite R:

First Target: First Target: SearchSearch We verify for DL-Lite R: We verify for DL-Lite R:

C:\Documents and Settings\Propietario\workspace\OwlGres21-jul-2008 12:58:45 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Total number of triples: 61921-jul-2008 12:58:45 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModelINFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owlThe TBox is compatible with DL-Lite

OWL-Gres is based OWL-Gres is based on DL-Lite Ron DL-Lite R

First Target: First Target: SearchSearch But… But…

We have concept attributes…We have concept attributes…

IS-A for concept attribuites?IS-A for concept attribuites? Range(Uc) IS-A Datatype NORange(Uc) IS-A Datatype NO Person IS-A domain(Uc) assertion NOPerson IS-A domain(Uc) assertion NO

First Target: First Target: ConclusionsConclusions

OWL-Gres is based on: OWL-Gres is based on:

DL-Lite RDL-Lite R

Concept attribuitesConcept attribuites

IS-A for concept attribuitesIS-A for concept attribuites

Second Target:Second Target: Preliminary notesPreliminary notes

2 t

ypes

of

sem

anti

c

2 t

ypes

of

sem

anti

c

GroundGround

StandardStandardq(x) q(x) ← ← y, z, w. edgeB(x,y) y, z, w. edgeB(x,y) edgeR(x,z) edgeR(x,z) edgeR(y,z) edgeR(y,z)

{a}

q(x) q(x) ← ← y, z, w. y, z, w. edgeB(x,y) edgeB(x,y) edgeR(x,z) edgeR(x,z) edgeR(y,z)edgeR(y,z)

q(x,y,z) q(x,y,z) ← ← y, z, w. y, z, w. edgeB(x,y) edgeB(x,y) edgeR(x,z) edgeR(x,z) edgeR(y,z)edgeR(y,z)

{}

{}

TBOXTBOX

edgeRedgeR--⊑⊑ NodeNodeedgeR edgeR ⊑⊑ NodeNodeedgeBedgeB--⊑⊑ NodeNodeedgeB edgeB ⊑⊑ NodeNodeNodeRB NodeRB ⊑ ⊑ edgeRedgeRNodeRB NodeRB ⊑⊑ edgeBedgeB

edgeB(a,a)edgeB(a,a)NodeRB(a)NodeRB(a)ABOXABOX

Second Target:Second Target: Preliminary notesPreliminary notes

Let’s consider the query 15:Let’s consider the query 15:

hasS

am

eH

om

eTow

nhasS

am

eH

om

eTow

nW

ith

Wit

h

X

Y Z

W

T

isMemberisMemberOfOf

hasMem

ber

hasMem

ber

isCrazyAbisCrazyAboutout

isCrazyAb

isCrazyAb

outout

The query is designed on The query is designed on purpose to establish if a purpose to establish if a reasoner is able to answer reasoner is able to answer according to the standard according to the standard conjunctive query semantic:conjunctive query semantic:1.1.Quonto gives out 94 answersQuonto gives out 94 answers2.2.OWLGres gives out 89 OWLGres gives out 89 answers, like Racer, Pellet, etc..answers, like Racer, Pellet, etc..

q(x) ← q(x) ← hasSameHomeTownWith(x,hasSameHomeTownWith(x,y) y) isMemberOf(y,z) isMemberOf(y,z) hasMember(z,t) hasMember(z,t) isCrazyAbout(t,w) isCrazyAbout(t,w) isCrazyAbout(x,w)isCrazyAbout(x,w)

Second Target:Second Target: Experiment conditionsExperiment conditions

We have made We have made two comparisons:two comparisons:

Without optimizationsWithout optimizations

With optimizationsWith optimizations What are they?What are they?

Keep the reasoners near as much as Keep the reasoners near as much as possible from the optimizations point of possible from the optimizations point of viewview


OptimizationsOptimizations

QUONTOQUONTO OWLGRESOWLGRES

Semantic conjunctive Semantic conjunctive query minimizationquery minimization

YesYes YesYes

Query containmentQuery containment YesYes NoNo

In-expansion In-expansion optimizationsoptimizations

YesYes NoNo

Auxiliar role Auxiliar role optimizationoptimization

YesYes YesYes

Selectivity Selectivity optimizationoptimization

NoNo YesYes


Semantic conjunctive query Semantic conjunctive query minimizationminimization

PeopleWithHobby ⊑ PeopleWithHobby ⊑ like like

q(x) :- PeopleWithHobby(x), like(x,y)q(x) :- PeopleWithHobby(x), like(x,y)

q(x) :- PeopleWithHobby(x)q(x) :- PeopleWithHobby(x)

likelike ⊑⊑ PeopleWithHobbyPeopleWithHobby

q(x) :- PeopleWithHobby(x), like(x,y)q(x) :- PeopleWithHobby(x), like(x,y)

q(x) :- like(x,y)q(x) :- like(x,y)

QuontoQuonto

QuontoQuonto

OWL-GresOWL-Gres





YesYes YesYes



YesYes NoNo


YesYes YesYes


NoNo YesYes


Query containmentQuery containment

We considered:We considered:

q(x):- A(x) q(x):- A(x) q(x) :- A(x),B(x) q(x) :- A(x),B(x)

We can send to evaluate We can send to evaluate

ONLY in QuontoONLY in Quonto

q(x):- A(x)q(x):- A(x)





YesYes YesYes



YesYes NoNo


YesYes YesYes


NoNo YesYes


In-expansion optimizationsIn-expansion optimizations

Man ⊑ Man ⊑ ¬¬WomanWoman

q(x):-Man(x),Woman(x)q(x):-Man(x),Woman(x)

answer {}answer {}

ONLY in QuontoONLY in Quonto

Consistent OntologyConsistent Ontology





YesYes YesYes



YesYes NoNo


YesYes YesYes


NoNo YesYes


Auxiliar role optimizationAuxiliar role optimization

For For A A ⊑⊑ R.CR.C It’s introduced an auxiliar roleIt’s introduced an auxiliar role

But has no membership assertionBut has no membership assertion

We delete all querys with We delete all querys with an an auxiliar roleauxiliar role

QuontoQuonto and and OWL-GresOWL-Gres





YesYes YesYes



YesYes NoNo


YesYes YesYes


NoNo YesYes


Selectivity optimizationSelectivity optimization

A concept, role or concept attribute has no A concept, role or concept attribute has no membership assertionsmembership assertions

We delete all the conjunctive queries with this We delete all the conjunctive queries with this elementelement

It’s correct ?It’s correct ?

ONLY in ONLY in OWL-GresOWL-Gres

Second Target:Second Target: First ComparisonFirst Comparison


Semantic Semantic conjunctive query conjunctive query

minimizationminimization

EnabledEnabled EnabledEnabled

query query containmentcontainment

DisabledDisabled --


DisabledDisabled --


-- DisabledDisabled


QueryExpansion

TimeEvaluation

TimeNumber of

Disjunctions

Nº ofdisjunctions 1ªOptimization Nºresults

Q O-G Q O-G Q O-G Q O-G Q O-G

q(x) ← GraduateStudent(x) takesCourse(x,”Dep0.Univ0/

GraduateCourse”) 0 16 16 31 2 2 1 1 12 12

q(x,y,z) ← GraduateStudent(x) University(y) Department(z)

subOrganizationOf(z,y) memberOf(x,z)

undergraduateDegreeFrom(x,y) 63 110 2422 7672 37 37 13 13103 103

q(x) ← Publication(x) publicationAuthor(x,”Dep0.Univ0/Assis

tantProfessor0”) 0 16 203 125 1 1 1 1 7 7

q(x,y1,y2,y3) ← Professor(x) worksFor(x,”Dep0.Univ0”)

name(x,y1) emailAddress(x,y2) telephone(x,y3) 47 125 1375 859 63 63 18 18 21 0


QueryExpansion

Time Evaluation Time

Number ofDisjunction

s

Nº ofdisjunctions 1ªOptimization Nºresults


q(x) ← Person(x) memberOf(x,”Dep0.Univ0”) 0 15 141 94 13 13 7 7 739 739

q(x) ← Student(x) 0 31 3516 2875 12 12 10 1013570

13570

q(x,y) ← Student(x) Course(y) takesCourse(x,y)

teacherOf(“Dep0.Univ0/AssociateProfessor0”,y) 0 16 188 125 4 4 2 2 45 45

q(x,y,z) ← Student(x) Department(y) memberOf(x,y)

subOrganizationOf(y,”Univ0”) emailAddress(x,z) 297 750 9687 36390 409 435 119 126

13320

13320

q(x,y,z) ← Student(x) Faculty(y) Course(z) advisor(x,y) teacherOf(y,z)

takesCourse(x,z) 0 31 266 2719 4 4 2 2 184 184

q(x) ← Student(x) takesCourse(x,”Dep0.Univ0/GraduateCourse

0”) 0 0 0 16 2 2 1 1 12 12


Query Expansion TimeEvaluation

TimeNumber of

disjunctions

Number of disjunctions

1ª optimitation Nºresults


q(x) ← ResearchGroup(x) subOrganizationOf(x,”Univ0”) 0 15 125 63 3 3 2 2 200 200

q(x,y) ← Chair(x) Department(y) worksFor(x,y)

subOrganizationOf(y,”Univ0”) 0 62 750 2235 20 20 4 4 20 20

q(x) ← Person(x) hasAlumnus(“Univ0”,x) 0 16 344 31 5 5 5 5 161 161

q(x) ← UndergraduateStudent(x) 0 16 31 500 1 1 1 11028

01028

0

q(x) ← hasSameHomeTownWith(x,y) isMemberOf(y,z) hasMember(z,t)

isCrazyAbout(t,w) isCrazyAbout(x,w) 3187 6219 3953 1469 6084 6084 49 49 89 89

Second Target:Second Target: QuontoQuonto AboxAbox

BaseballFanBaseballFan Concept:Concept:


iscrazyaboutiscrazyabout Role: Role:


e-mail attribute of concept:e-mail attribute of concept:

Second Target:Second Target: OWL-GresOWL-Gres AboxAbox

TBOX_name:TBOX_name:


TBOX_Concept_inclusion :TBOX_Concept_inclusion :


Individual_name :Individual_name :


Concept_assertion :Concept_assertion :


Object_Role_assertion: Object_Role_assertion:


Data_Role_assertion :Data_Role_assertion :

Second Target:Second Target: Second ComparisonSecond Comparison


Semantic Semantic conjunctive query conjunctive query

minimizationminimization

EnabledEnabled EnabledEnabled

query query containmentcontainment

EnabledEnabled --


EnabledEnabled --


-- EnabledEnabled


QueryExpansion

TimeEvaluation

Time

Number of

disjunctions

Nº of disjunctions

1ª optimizatio

n

Nº ofdisjuncti

ons selectivit

yNº of

results

Q O-G Q O-G QO-G Q O-G Q

O-G Q O-G

q(x) ← GraduateStudent(x) takesCourse(x,”Dep0.Univ0/Graduate

Course0”) 16 0 78 32 2 2 1 1 1 1 12 12

q(x,y,z) ← GraduateStudent(x) University(y) Department(z)

subOrganizationOf(z,y) memberOf(x,z)

undergraduateDegreeFrom(x,y) 141 94 688 203 35 37 13 13 13 9 103 103

q(x) ← Publication(x) publicationAuthor(x,”Dep0.Univ0/As

sistantProfessor0”) 0 0 16 16 1 1 1 1 1 1 7 7


QueryExpansion

TimeEvaluation

Time

Number of

disjunctions

Nº of disjunction

s 1ª optimizatio

n

Nº ofdisjuncti

ons selectivit

yNº of

results


O-G Q O-G

q(x,y1,y2,y3) ← Professor(x) worksFor(x,”Dep0.Univ0”)

name(x,y1) emailAddress(x,y2) telephone(x,y3) 78 125 1375 859 63 63 18 18 18 10 21 0

q(x) ← Person(x) memberOf(x,”Dep0.Univ0”) 16 15 62 63 13 13 7 7 7 5 739 739

q(x) ← Student(x) 0 31 1906 703 12 12 10 10 10 5 13570 13570

q(x,y) ← Student(x) Course(y) takesCourse(x,y)

teacherOf(“Dep0.Univ0/AssociateProfessor0”,y) 0 16 16 31 4 4 2 2 2 2 45 45


QueryExpansion

TimeEvaluation

Time

Nº of disjunctio

ns

1ª opt: Nº of

disjunctions

Selec. Nº of disjunction

s Nº results


O-G Q O-G

q(x,y,z) ← Student(x) Department(y) memberOf(x,y) subOrganizationOf(y,”Univ0”)

emailAddress(x,z) 860 766 3031 17438 310 435 85 126 85 4513320

13320

q(x,y,z) ← Student(x) Faculty(y) Course(z) advisor(x,y)

teacherOf(y,z) takesCourse(x,z) 15 31 62 188 4 4 2 2 2 2 184 184

q(x) ← Student(x) takesCourse(x,”Dep0.Univ0/Grad

uateCourse0” 0 16 0 31 2 2 1 1 1 1 12 12

q(x) ← ResearchGroup(x) subOrganizationOf(x,”Univ0”) 0 15 16 31 3 3 2 2 2 1 200 200


Query Expansion TimeEvaluation

TimeNº of

disjunctions

1ª opt: Nº of

disjunctions

Selec. Nº of

disjunctions Nº results

Q O-G Q O-G Q O-G Q O-G QO-G Q O-G

q(x,y) ← Chair(x) Department(y) worksFor(x,y)

subOrganizationOf(y,”Univ0”) 0 47 672 2172 19 20 4 4 4 4 20 20

q(x) ← Person(x) hasAlumnus(“Univ0”,x) 0 16 16 31 5 5 5 5 5 3 161 161

q(x) ← UndergraduateStudent(x) 0 16 63 515 1 1 1 1 1 1

10280

10280

q(x) ← hasSameHomeTownWith(x,y)

isMemberOf(y,z) hasMember(z,t)

isCrazyAbout(t,w) isCrazyAbout(x,w) 123266 6219 672 1469 6084 6084 49 49 49 25 89 89

ConclusionsConclusionsQUONTOQUONTO OWLGRESOWLGRES

ABOX sizeABOX size

Evaluation TimeEvaluation Time

Expansion TimeExpansion Time


55 MB75 MB