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OWL-Gres vs Quonto. Angela Alvarez Rubio. Introduction. Using ontologies as a conceptual point of view on repositories of data is increasingly. These ontologies deal with large amounts of data. Most important parameter on computational complexity of reasoning. Data size.

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owl gres vs quonto

OWL-Gres vs Quonto

Angela Alvarez Rubio

introduction
Introduction
  • Using ontologies as a conceptual point of view on repositories of data is increasingly
  • These ontologies deal with large amounts of data
  • Most important parameter on computational complexity of reasoning
  • Data size
  • We will want a polynomial reasoning!
  • And we want can do complex questions
introduction1
Introduction
  • 2 stapes:
  • 1. Perfect reformulation: taking into account the TBOX T, the q query is reformulated in a new query
  • On a DL-Lite
  • Conjunctive query is a union of conjunctive wich size does not depend on A
  • We can evaluate it with LOGSPACE on the ABOX size
introduction2
Introduction
  • 2 stapes:
  • 2. Query Evaluation: the new query is evaluated only in the ABox to produce the answer
  • The ABox, is maintained through a RDBMS (Data management systems relational) in the secondary storage to control a large data number
  • Because is the unique tecnology
  • The evaluation of the query can be delegated to an engine SQL database with optimization of querys strategies
introduction3
Introduction
  • We presented two systems to work with large amounts of data:
  • OWL-Gres
  • Quonto
introduction4
Introduction
  • Targets
  • Discover the DL-Lite fragment in which is based in OWL_Gres
  • Compare the OWL-Gres system with Quonto system
quonto
Quonto
  • Is a tool that implements the DL-Lite query answering algorithm
  • Delegates to a RBDMS the storing of the ABOX
  • Is capable of answering questions about ABOXes wich containing millions of assertions
  • Their limitations will depend of the single engine DBM
quonto dl lite a
Quonto: DL-Lite A+
  • Is the fragment DL-Lite largest known in order to obtain LOGSPACE data complexity
  • Represents the domain in terms of concepts, sets of objects, and roles and permets:
  • Value-Domains: domains that denote specific sets of values (data)
  • Concept attributes: binary relations between objects and values
  • Role attributes: ternary relations between pairs of objects and value
  • Enjoys FOL-rewritability
  • Allows for functionality assertions and role inclusion assertions, but with some restrictions:
  • No functional role or attribute can be specialized by using it in the right-hand side of a role or attribute inclusion assertions
quonto dl lite a1
Quonto: DL-Lite A+
  • Concept inclusion assertion: B ⊑ C

The knowledge base (KB) is formed by:

  • Attribute inclusion assertion: U ⊑ V
  • T: TBOX to represent intensional knowledge
  • Value-domain inclusion assertion: E ⊑ F
  • Role inclusion assertion: Q ⊑ R
  • Attribute functionality assertion: funct U
  • Role functionality assertion: funct Q
  • Attribute Role: funct R

K=<T, A>

  • A: ABOX to represent extensional knowledge
  • Member Assertions

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

UC(c; d) UR(a, b, c)

quonto query answering
Quonto:Query answering
  • Query conjunctive in a KB K:
  • Union of conjunctive queries (UCQ):
  • x: Distinguished variables
  • y: Non-distinguished variables
  • conj (x, y): atoms:
    • A(xo)
    • P(xo, yo)
    • D(xv)
    • UC(xo,xv)
    • UR(xo,y0 xv)

q(x) ←y. conj(x,y)

  • xo, yo are variables in x and y or constants in ГO
  • xv is a variable in x and y a constant in ГV

Certain answers all tuples t of elements of ГV ГO such that, when substituted to x in q(x), we have that K |= q(t)

q(x) ←Viyi. conj(x,y)

firs target
Firs Target
  • On what DL-Lite fragment is based OWL-Gres?
  • See the characteristics of potential fragments and differentiate it
  • 2 steps:
  • Java Program
  • See if OWL-Gres accept this characteristics
  • Protege tool
first target search
First Target: Search
  • We use a TBOX based in the university hierarchy:
first target search1
First Target: Search
  • Initially our TBOX is compatible with OWL-Gres:

C:\Documents and Settings\Propietario\workspace\OwlGres

21-jul-2008 12:54:42 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Total number of triples: 617

21-jul-2008 12:54:42 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owl

The TBox is compatible with DL-Lite

first target search2
First Target: Search
  • We verify for DL-Lite F:
first target search3
First Target: Search
  • We verify for DL-Lite F:

C:\Documents and Settings\Propietario\workspace\OwlGres

21-jul-2008 12:57:25 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Total number of triples: 618

21-jul-2008 12:57:25 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owl

FRAGMENT ERROR: No support for axiom OWLFunctionalObjectPropertyAxiom

On OWL Axiom: FunctionalObjectProperty(takesCourse)

The TBox is not compatible with DL-Lite

The TBos is not compatible with DL-Lite FR or DL-Lite A

DL-Lite F

DL-Lite FR

DL-Lite A

DL-Lite R

first target search4
First Target: Search
  • We verify for DL-Lite R:
first target search5
First Target: Search
  • We verify for DL-Lite R:

C:\Documents and Settings\Propietario\workspace\OwlGres

21-jul-2008 12:58:45 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Total number of triples: 619

21-jul-2008 12:58:45 org.coode.owl.rdfxml.parser.OWLRDFConsumer endModel

INFO: Loaded http://semantics.crl.ibm.com/univ-bench-dl.owl

The TBox is compatible with DL-Lite

OWL-Gres is based on DL-Lite R

first target search6
First Target: Search
  • But…
    • We have concept attributes…
  • IS-A for concept attribuites?
  • Range(Uc) IS-A Datatype NO
  • Person IS-A domain(Uc) assertion NO
first target conclusions
First Target: Conclusions
  • OWL-Gres is based on:
  • DL-Lite R
  • Concept attribuites
  • IS-A for concept attribuites
second target preliminary notes
Second Target:Preliminary notes

edgeR-⊑Node

edgeR ⊑Node

edgeB-⊑Node

edgeB ⊑Node

NodeRB ⊑ edgeR

NodeRB ⊑edgeB

edgeB(a,a)

NodeRB(a)

ABOX

TBOX

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

{a}

  • Standard

2 types of semantic

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

  • Ground

{}

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

{}

second target preliminary notes1
Second Target:Preliminary notes

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

Let’s consider the query 15:

isMemberOf

Y

Z

hasMember

The query is designed on purpose to establish if a reasoner is able to answer according to the standard conjunctive query semantic:

Quonto gives out 94 answers

OWLGres gives out 89 answers, like Racer, Pellet, etc..

hasSameHomeTownWith

T

isCrazyAbout

X

W

isCrazyAbout

second target experiment conditions
Second Target:Experiment conditions
  • We have made two comparisons:
  • Without optimizations
  • Keep the reasoners near as much as possible from the optimizations point of view
  • With optimizations
  • What are they?
second target experiment conditions2
Second Target:Experiment conditions

Semantic conjunctive query minimization

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

PeopleWithHobby ⊑  like

Quonto

q(x) :- PeopleWithHobby(x)

Quonto

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

 like⊑PeopleWithHobby

OWL-Gres

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

second target experiment conditions4
Second Target:Experiment conditions

Query containment

We considered:

q(x):- A(x)  q(x) :- A(x),B(x)

We can send to evaluate

q(x):- A(x)

ONLY in Quonto

second target experiment conditions6
Second Target:Experiment conditions

In-expansion optimizations

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

Consistent Ontology

answer {}

Man ⊑ ¬Woman

ONLY in Quonto

second target experiment conditions8
Second Target:Experiment conditions

Auxiliar role optimization

  • For A ⊑R.C

Quonto and OWL-Gres

  • It’s introduced an auxiliar role
  • But has no membership assertion
  • We delete all querys with an auxiliar role
second target experiment conditions10
Second Target:Experiment conditions

Selectivity optimization

ONLY in OWL-Gres

  • A concept, role or concept attribute has no membership assertions
  • We delete all the conjunctive queries with this element
  • It’s correct ?
second target quonto abox
Second Target: QuontoAbox

BaseballFanConcept:

second target quonto abox1
Second Target: QuontoAbox

iscrazyabout Role:

second target quonto abox2
Second Target: QuontoAbox

e-mail attribute of concept:

second target owl gres abox1
Second Target: OWL-GresAbox

TBOX_Concept_inclusion :

second target owl gres abox2
Second Target: OWL-GresAbox

Individual_name :

second target owl gres abox3
Second Target: OWL-GresAbox

Concept_assertion :

second target owl gres abox4
Second Target: OWL-GresAbox

Object_Role_assertion:

second target owl gres abox5
Second Target: OWL-GresAbox

Data_Role_assertion :

conclusions
Conclusions

75 MB

55 MB

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