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PAGOdA poster

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Abstract: An enhanced hybrid approach to OWL query answering that combines an RDF triple-store with an OWL reasoner in order to provide scaleable pay-as-you-go performance. The enhancements presented here include an extension to deal with arbitary OWL ontologies and optimisations that significantly improve scalability. We have implemented these techniques in a prototype system, a preliminary evaluation of which has produced very encouraging results.

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PAGOdA poster

  1. 1. Pay-as-you-go OWL Query Answering Using a Triple Store Yujiao Zhou, Yavor Nenov, Bernardo Cuenca Grau and Ian Horrocks Problem Setting ‣ Ontology Σ — a set of rules of the form φ(x) → Vi ∃yi ψ(x, yi) ‣ Data D — a set of ground atoms of the form P(a) ‣ Conjunctive queries — FO formula of the form q(x) ← ∃y ψ(x, y) where ψ and φ are conjunctions of atoms. Pay-as-you-go Approach Intuition ‣ to delegate the bulk of the computational workload to a highly scalable datalog reasoner ! ‣ to minimise the use of a fully-fledged reasoner Diagram Over-approx to datalog ‣ Disjunctive knowledge Evaluation ‣ Existential knowledge {A, . . .} ‣ Evaluated on LUBM(100,1000), UOBM(1, 60, 500), FLY, DBPedia+travel and NPD FactPages. Average time without OWL 2 reasoning triple store OWL 2 reasoner ELHO Lower Upper Data Ontology L=LRL ∪ LEL ∪ … U U D Query Datalog Engine Fragment Summarisation Summary Datalog Engine Datalog Engine Full Reasoner Q F Dependency Analysis F Full Reasoner Q Output L = U Tracking by datalog encoding σ(cert(q, F)) ⊆ cert(q, σ(F)) Rule out non-answers Incomplete endomorphisms Arrange calls to the reasoner according to the dependencies heuristically Acknowledgements Average time Lower Data Done This work was supported by the Royal Society, the EPSRC projects Score!, ExODA, and MaSI3, and the FP7 project OPTIQUE. ! ! ! ! ‣ upper bound U answer of q w.r.t the resulting set of rules U(Σ) and D. Lower bounds ‣ basic lower bound LRL answer of q w.r.t. the datalog fragment of Σ and D; ‣ EL lower bound LEL answer of q w.r.t. the ELHO fragment of Σ and D. Tracking encoding in datalog Intuition: to compute all the rules and facts that participate in a proof of q(a) in Σ∪D. This goal can be archived using datalog encoding. ‣ Example: ‣ If B1(x1),…,Bm(xm) → H(x) is a rule in U(Σ), Ht(x), B1 (x1), . . . , Bm (xm) → S(cr)∧B1t (x1 )∧ . . . ∧Bmt(xm ) is added to the tracking rule. ‣ Involved rules: {r | S(cr) is derived} Involved facts: {P(a) ∈ D | Pt(a) is derived} Summarisation & dependency between answers ‣ Let σ be the summary function, σ(cert(q, F)) ⊆ cert(q, σ(F)) ‣ If there is an endomorphism from a to b in F, then a ∈ cert(q, F) implies b ∈ cert(q, F) {. . . , A u B, . . .} {C} {C} x1 {A, . . .} x2 R R {A, . . .} c {C} x1 {A, . . .} x2 R R {. . . , A t B, . . .} DL Ontology Dataset Queries LUBM(n) SHI 93 ~100,000n 14 (std)+10 UOBM(n) SHIN 314 ~200,000n 1!5 FLY SRI 144,407 6,308 88 5 DBPedia SHOIN 1,757 12,119,662 441 (atomic) NPD SHIF 819 3,817,079 329 (atomic) LUBM(1000) UOBM(100) FLY DBPedia NPD Queries 22/24 12/15 5/5 439/441 294/329 Time(s) 18.4 0.7 0.2 0.3 0.1 LUBM(100) UOBM(1) FLY DBPedia NPD Time(s) 29.6 1.8 0.2 3 3

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