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OWL-Gres vs Quonto

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

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  1. OWL-Gres vs Quonto Angela Alvarez Rubio

  2. 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

  3. 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

  4. 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

  5. Introduction • We presented two systems to work with large amounts of data: • OWL-Gres • Quonto

  6. Introduction • Targets • Discover the DL-Lite fragment in which is based in OWL_Gres • Compare the OWL-Gres system with Quonto system

  7. 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

  8. 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

  9. 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)

  10. 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)

  11. 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

  12. First Target: Fragments

  13. First Target: Fragments

  14. First Target: Fragments

  15. First Target: Fragments

  16. First Target: Fragments

  17. First Target: Fragments

  18. First Target: Fragments

  19. First Target: Search • We use a TBOX based in the university hierarchy:

  20. 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

  21. First Target: Search • We verify for DL-Lite F:

  22. 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

  23. First Target: Search • We verify for DL-Lite R:

  24. 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

  25. 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

  26. First Target: Conclusions • OWL-Gres is based on: • DL-Lite R • Concept attribuites • IS-A for concept attribuites

  27. 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) {}

  28. 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

  29. 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?

  30. Second Target:Experiment conditions Optimizations

  31. 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)

  32. Second Target:Experiment conditions Optimizations

  33. 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

  34. Second Target:Experiment conditions Optimizations

  35. Second Target:Experiment conditions In-expansion optimizations q(x):-Man(x),Woman(x) Consistent Ontology answer {} Man ⊑ ¬Woman ONLY in Quonto

  36. Second Target:Experiment conditions Optimizations

  37. 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

  38. Second Target:Experiment conditions Optimizations

  39. 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 ?

  40. Second Target: First Comparison

  41. Second Target:First Comparison

  42. Second Target:First Comparison

  43. Second Target:First Comparison

  44. Second Target: QuontoAbox BaseballFanConcept:

  45. Second Target: QuontoAbox iscrazyabout Role:

  46. Second Target: QuontoAbox e-mail attribute of concept:

  47. Second Target: OWL-GresAbox TBOX_name:

  48. Second Target: OWL-GresAbox TBOX_Concept_inclusion :

  49. Second Target: OWL-GresAbox Individual_name :

  50. Second Target: OWL-GresAbox Concept_assertion :

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