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FOUNDATIONS OF SEMANTIC WEB TECHNOLOGIES Hypertableau I Sebastian Rudolph

Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

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Page 1: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

FOUNDATIONS OF SEMANTICWEB TECHNOLOGIES

Hypertableau I

Sebastian Rudolph

Page 2: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Hypertableau I

TU Dresden Foundations of Semantic Web Technologies

Page 3: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Hypertableau I

TU Dresden Foundations of Semantic Web Technologies

Page 4: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

TU Dresden Foundations of Semantic Web Technologies

Page 5: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

TU Dresden Foundations of Semantic Web Technologies

Page 6: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

Let the following TBox T and ABox A be given:

T = {∃r.A v A} CT = ∀r.(¬A) t A

A = {¬A(a0), r(a0, b1), r(b1, a1), . . . , r(an−1, bn), r(bn, an), A(an)}

a0 b1 a1 b2 an−1 bn anr r r r r

Assumption: we address the nodes in the tableau in alphabetic order, i.e., a’sbefore b’s

TU Dresden Foundations of Semantic Web Technologies

Page 7: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT }

∪ {∀r.(¬A)1}

L(a1) = {CT }

∪ {∀r.(¬A)2} ∪ {¬An+2}

......

L(an−1) = {CT }

∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT }

∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT }

∪ {¬A2} ∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 8: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT }

∪ {∀r.(¬A)2} ∪ {¬An+2}

......

L(an−1) = {CT }

∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT }

∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT }

∪ {¬A2} ∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 9: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT }

∪ {∀r.(¬A)2} ∪ {¬An+2}

......

L(an−1) = {CT }

∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT }

∪ {¬A2} ∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 10: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT }

∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT }

∪ {¬A2} ∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 11: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT }

∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 12: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT }

∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 13: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT }

∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 14: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1}

∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 15: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2}

∪ {¬An+2}

......

L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 16: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2}

∪ {∀r.(¬A)n+3}

......

L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 17: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n}

∪ {¬A2n} ∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 18: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An}

∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 19: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1}

∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1}

∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 20: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1}

∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 21: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 22: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 23: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n}

∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 24: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Example Standard Tableau

CT = ∀r.(¬A) t A

a0 b1 a1 b2 an−1 bn anr r r r r

L(a0) = {¬A, CT } ∪ {∀r.(¬A)1}

L(a1) = {CT } ∪ {∀r.(¬A)2} ∪ {¬An+2}...

...L(an−1) = {CT } ∪ {∀r.(¬A)n} ∪ {¬A2n} ∪ {A}

L(an) = {A, CT } ∪ {∀r.(¬A)n+1} ∪ {¬A2n+1}

L(b1) = {CT } ∪ {¬A1} ∪ {∀r.(¬A)n+2}

L(b2) = {CT } ∪ {¬A2} ∪ {∀r.(¬A)n+3}...

...L(bn) = {CT } ∪ {¬An} ∪ {∀r.(¬A)2n+1} ∪ {A2n+1}

Now again decisions for an and b1, . . . , bn

TU Dresden Foundations of Semantic Web Technologies

Page 25: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Is that Necessarily So?

• the algorithm constructs exponentially many branches, despitedependency directed backtracking

• translation of the formula into FOL:

∀r.(¬A) t A

= ∀x, y. [r(x, y) ∧ A(y)→ A(x)]

= ∀x, y. [¬r(x, y) ∨ ¬A(y) ∨ A(x)]

• note: the formula does not have real non-determinism (Horn-clause)• hypertableau exploits this

TU Dresden Foundations of Semantic Web Technologies

Page 26: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Idea Hypertableau

• translate KB axioms into FOL– rewrite axioms to obtain formulae of a certain structure

• axioms are translated such that non-determinism is avoided, if possible• the formulae thus obtained become rules for constructing a model

abstraction

TU Dresden Foundations of Semantic Web Technologies

Page 27: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′

A(x)→ A′(x)

A v ∃r.B

A(x)→ ∃r.B(x)

D v E t F

D(x)→ E(x) ∨ F(x)

F v ⊥

F(x)→ ⊥(x)

∃r.> v C

r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 28: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B

A(x)→ ∃r.B(x)

D v E t F

D(x)→ E(x) ∨ F(x)

F v ⊥

F(x)→ ⊥(x)

∃r.> v C

r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 29: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F

D(x)→ E(x) ∨ F(x)

F v ⊥

F(x)→ ⊥(x)

∃r.> v C

r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 30: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F D(x)→ E(x) ∨ F(x)

F v ⊥

F(x)→ ⊥(x)

∃r.> v C

r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 31: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F D(x)→ E(x) ∨ F(x)

F v ⊥ F(x)→ ⊥(x)∃r.> v C

r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 32: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F D(x)→ E(x) ∨ F(x)

F v ⊥ F(x)→ ⊥(x)∃r.> v C r(x, y)→ C(x)

A(a)

→ A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 33: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F D(x)→ E(x) ∨ F(x)

F v ⊥ F(x)→ ⊥(x)∃r.> v C r(x, y)→ C(x)

A(a) → A(a)

(D u ¬B)(d)

→ D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 34: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

1 Translation into Clauses

A v A′ A(x)→ A′(x)

A v ∃r.B A(x)→ ∃r.B(x)D v E t F D(x)→ E(x) ∨ F(x)

F v ⊥ F(x)→ ⊥(x)∃r.> v C r(x, y)→ C(x)

A(a) → A(a)

(D u ¬B)(d) → D(d)

→ ¬B(d)

• existential quantifiers treated as in the tableau

TU Dresden Foundations of Semantic Web Technologies

Page 35: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A}

∪ {A′} ∪ {∃r.B} ∪ {C}

L(d) = {D,¬B}

∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 36: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A}

∪ {A′} ∪ {∃r.B} ∪ {C}

L(d) = {D,¬B}

∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 37: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′}

∪ {∃r.B} ∪ {C}

L(d) = {D,¬B}

∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 38: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′}

∪ {∃r.B} ∪ {C}

L(d) = {D,¬B}

∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 39: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′}

∪ {∃r.B} ∪ {C}

L(d) = {D,¬B} ∪ {E1}

L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 40: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′}

∪ {∃r.B} ∪ {C}

L(d) = {D,¬B} ∪ {E1}

L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 41: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′}

∪ {∃r.B} ∪ {C}

L(d) = {D,¬B} ∪ {E1}

L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 42: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′} ∪ {∃r.B}

∪ {C}

L(d) = {D,¬B} ∪ {E1}

L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 43: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′} ∪ {∃r.B}

∪ {C}

L(d) = {D,¬B} ∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 44: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′} ∪ {∃r.B}

∪ {C}

L(d) = {D,¬B} ∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 45: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′} ∪ {∃r.B} ∪ {C}

L(d) = {D,¬B} ∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 46: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Simple Hypertableau Example

a

v1

d

r

L(a) = {A} ∪ {A′} ∪ {∃r.B} ∪ {C}

L(d) = {D,¬B} ∪ {E1}L(v1) = {B}

A(x) → A′(x)A(x) → ∃r.B(x)D(x) → E(x) ∨ F(x)F(x) → ⊥(x)

r(x, y) → C(x)

→ A(a)→ D(d)→ ¬B(d)

• no more rules applicable satisfiability has been shown• the only thing left from the tableau rules: an analogue of the ∃-rule

TU Dresden Foundations of Semantic Web Technologies

Page 47: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

TU Dresden Foundations of Semantic Web Technologies

Page 48: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Translation into FOLtranslation of TBox axioms into FOL via the mapping π with C, D complexclasses, r a role and A an atomic class:

π(C v D) = ∀x.(πx(C)→ πx(D)) π(C ≡ D) = ∀x.(πx(C)↔ πx(D))

πx(A) = A(x) πy(A) = A(y)

πx(¬C) = ¬πx(C) πy(¬C) = ¬πy(C)

πx(C u D) = πx(C) ∧ πx(D) πy(C u D) = πy(C) ∧ πy(D)

πx(C t D) = πx(C) ∨ πx(D) πy(C t D) = πy(C) ∨ πy(D)

πx(∀r.C) = ∀y.(r(x, y)→ πy(C)) πy(∀r.C) = ∀x.(r(y, x)→ πx(C))

πx(∃r.C) = ∃y.(r(x, y) ∧ πy(C)) πy(∃r.C) = ∃x.(r(y, x) ∧ πx(C))

TU Dresden Foundations of Semantic Web Technologies

Page 49: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 50: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 51: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 52: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 53: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 54: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

Page 55: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Motivation Normal Form

• the given translation creates rather complex formulae for complex axioms

π(C v ∃r.(∀s.(D t ∃r.D)))

∀x. [πx(C)→ πx(∃r.(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ πy(∀s.(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D t ∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ πx(D) ∨ πx(∃r.D)))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ πy(D))))]

∀x. [C(x)→ ∃y.(r(x, y) ∧ ∀x.(s(y, x)→ D(x) ∨ ∃y.(r(x, y) ∧ D(y))))]

TU Dresden Foundations of Semantic Web Technologies

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Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

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Structural Transformation

• Structural Transformation introduces new concepts for complexsubconcepts

C v ∃r.(∀s.(D t ∃r.D))

C v ∃r.Q1

Q1 ≡ ∀s.(D t ∃r.D)

Q1 ≡ ∀s.Q2

Q2 ≡ D t ∃r.D Q2 ≡ D t Q3

Q3 ≡ ∃r.D

TU Dresden Foundations of Semantic Web Technologies

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Structural Transformation

• Structural Transformation introduces new concepts for complexsubconcepts

C v ∃r.(∀s.(D t ∃r.D))

C v ∃r.Q1

Q1 ≡ ∀s.(D t ∃r.D)

Q1 ≡ ∀s.Q2

Q2 ≡ D t ∃r.D Q2 ≡ D t Q3

Q3 ≡ ∃r.D

TU Dresden Foundations of Semantic Web Technologies

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Structural Transformation

• Structural Transformation introduces new concepts for complexsubconcepts

C v ∃r.(∀s.(D t ∃r.D))

C v ∃r.Q1

Q1 ≡ ∀s.(D t ∃r.D)

Q1 ≡ ∀s.Q2

Q2 ≡ D t ∃r.D

Q2 ≡ D t Q3

Q3 ≡ ∃r.D

TU Dresden Foundations of Semantic Web Technologies

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Structural Transformation

• Structural Transformation introduces new concepts for complexsubconcepts

C v ∃r.(∀s.(D t ∃r.D))

C v ∃r.Q1

Q1 ≡ ∀s.(D t ∃r.D)

Q1 ≡ ∀s.Q2

Q2 ≡ D t ∃r.D Q2 ≡ D t Q3

Q3 ≡ ∃r.D

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B)

∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B)

∃r.(A u B) v C

A v ∀r.Q

∃r.Q v C

Q v ∀r.B

X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B)

∃r.(A u B) v C

A v ∀r.Q

∃r.Q v C

Q v ∀r.B X

Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q

∃r.Q v C

Q v ∀r.B X

Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}

• In the context of a TBox with the original axiom, the ABox wascontradictory.

• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.

• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimization of Structural Transformation

• do we have to introduce equivalence axioms?

A v ∀r.(∀r.B) ∃r.(A u B) v C

A v ∀r.Q ∃r.Q v C

Q v ∀r.B X Q v A u B

Q v A

Q v B

Well. Is that correct? Let A = {r(a, b), A(b), B(b),¬C(a)}• In the context of a TBox with the original axiom, the ABox was

contradictory.• In the context of a TBox with the rewritten axiom, the ABox is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Polarity for Optimized Transformation

• we have to take care if subexpressions occur “positively” or “negatively”• A v B is just ¬A t B

• thus, A occurs negatively in the axiom and B positively• when replacing a negatively occurring concept, we have to use w

∃r.(A u B) v C

∃r.Q v C

Q w A u B

A u B v Q

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

TU Dresden Foundations of Semantic Web Technologies

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Polarity for Optimized Transformation

• we have to take care if subexpressions occur “positively” or “negatively”• A v B is just ¬A t B

• thus, A occurs negatively in the axiom and B positively• when replacing a negatively occurring concept, we have to use w

∃r.(A u B) v C

∃r.Q v C

Q w A u B

A u B v Q

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

TU Dresden Foundations of Semantic Web Technologies

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Polarity for Optimized Transformation

• we have to take care if subexpressions occur “positively” or “negatively”• A v B is just ¬A t B

• thus, A occurs negatively in the axiom and B positively• when replacing a negatively occurring concept, we have to use w

∃r.(A u B) v C

∃r.Q v C

Q w A u B

A u B v Q

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

TU Dresden Foundations of Semantic Web Technologies

Page 73: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Polarity for Optimized Transformation

• we have to take care if subexpressions occur “positively” or “negatively”• A v B is just ¬A t B

• thus, A occurs negatively in the axiom and B positively• when replacing a negatively occurring concept, we have to use w

∃r.(A u B) v C

∃r.Q v C

Q w A u B

A u B v Q

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

TU Dresden Foundations of Semantic Web Technologies

Page 74: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Polarity for Optimized Transformation

• we have to take care if subexpressions occur “positively” or “negatively”• A v B is just ¬A t B

• thus, A occurs negatively in the axiom and B positively• when replacing a negatively occurring concept, we have to use w

∃r.(A u B) v C

∃r.Q v C

Q w A u B

A u B v Q

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C}

∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}

L(b) = {A, B}

∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B}

∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q}

∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A}

∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A}

∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B}

∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B}

∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

Page 84: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Tableau for Unsatisfiability

CT = (∀r.(¬Q) t C) u (¬A t ¬B t Q)

A = {r(a, b), A(b), B(b),¬C(a)}

a

b

r

L(a) = {¬C} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q, ∀r.(¬Q),¬A}L(b) = {A, B} ∪ {¬Q} ∪ {CT ,∀r.(¬Q) t C,¬A t ¬B t Q,∀r.(¬Q)}

∪ {¬A} ∪ {¬B} ∪ {Q}

no further choice options the KB is satisfiable

TU Dresden Foundations of Semantic Web Technologies

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Optimized Structural Transformation

• we now want to define structural transformation formally• we want to introduce just one new concept name per subexpression• goal: rewrite TBox into an equisatisfiable TBox umzuschreiben containing

only “simple” axioms

TU Dresden Foundations of Semantic Web Technologies

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Polarity of Concepts

We define the polarity of a concept C inside a formula as follows:• C occurs in C positively,• C occurs in ¬D positively (negatively) if C occurs in D negatively

(positively),• C occurs in D u E or D t E positively (negatively), if C occurs positively

(negatively) in D or E,• C occurs in ∃r.D or ∀r.D positively (negatively), if C occurs positively

(negatively) in D,• C occurs in D v E positively (negatively), if C occurs positively

(negatively) in E or negatively (positively) in D.

A concept occurs positively (negatively) in an (ALC) TBox T , if C occurspositively (negatively) in an axiom in T . a concept may occur both positively and negatively in an axiom

TU Dresden Foundations of Semantic Web Technologies

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Polarity of Concepts

We define the polarity of a concept C inside a formula as follows:• C occurs in C positively,• C occurs in ¬D positively (negatively) if C occurs in D negatively

(positively),• C occurs in D u E or D t E positively (negatively), if C occurs positively

(negatively) in D or E,• C occurs in ∃r.D or ∀r.D positively (negatively), if C occurs positively

(negatively) in D,• C occurs in D v E positively (negatively), if C occurs positively

(negatively) in E or negatively (positively) in D.

A concept occurs positively (negatively) in an (ALC) TBox T , if C occurspositively (negatively) in an axiom in T .

a concept may occur both positively and negatively in an axiom

TU Dresden Foundations of Semantic Web Technologies

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Polarity of Concepts

We define the polarity of a concept C inside a formula as follows:• C occurs in C positively,• C occurs in ¬D positively (negatively) if C occurs in D negatively

(positively),• C occurs in D u E or D t E positively (negatively), if C occurs positively

(negatively) in D or E,• C occurs in ∃r.D or ∀r.D positively (negatively), if C occurs positively

(negatively) in D,• C occurs in D v E positively (negatively), if C occurs positively

(negatively) in E or negatively (positively) in D.

A concept occurs positively (negatively) in an (ALC) TBox T , if C occurspositively (negatively) in an axiom in T . a concept may occur both positively and negatively in an axiom

TU Dresden Foundations of Semantic Web Technologies

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Optimized Transformation with Polarity

Let T be an ALC TBox. For every concept (sub-)expression C in T , weintroduce a fresh atomic concept AC and define the function st(C) as follows:

st(A) = A st(¬C) = ¬AC st(∃r.C) = ∃r.AC

st(>) = > st(C u D) = AC u AD st(∀r.C) = ∀r.AC

st(⊥) = ⊥ st(C t D) = AC t AD

The result of the structural transformation of a TBox T is a TBox T ′ with thefollowing axioms:• AC v st(C) for every concept C occurring positively in T ,• st(C) v AC for every concept C occurring negatively in T ,• AC v AD for every GCI C v D ∈ T .

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Simplification of Axioms

we can now use known equivalences to simplify the axioms further:

{C v D u E} ≡ {C v D, C v E}{C t D v E} ≡ {C v E, D v E}

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Result of the Structural Transformationby virtue of structural transformation and the known equivalences, we canrewrite an ALC KB into an equisatisfiable one, which contains only axioms ofthe following shape (A and B being atomic):

A1 u . . . u An v B1 t . . . t Bm

A v ∃r.BA v ∀r.B

∃r.A v B

∀r.A v B

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1

C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1

C6 v ∀r.A

C2 v C u E

C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1

C6 v ∀r.A

C2 v C u E

C3 u A u C4 v C7

∀r.C1 v C3

C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1

C6 v ∀r.A

C2 v C u E

C3 u A u C4 v C7

∀r.C1 v C3

C8 v C5 u F

∃s.C1 v C4

C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1

C6 v ∀r.A

C2 v C u E

C3 u A u C4 v C7

∀r.C1 v C3

C8 v C5 u F

∃s.C1 v C4

C9 v B t C8 t C6

C5 v ∃r.C3

C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E

C3 u A u C4 v C7

∀r.C1 v C3

C8 v C5 u F

∃s.C1 v C4

C9 v B t C8 t C6

C5 v ∃r.C3

C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3

C8 v C5 u F

∃s.C1 v C4

C9 v B t C8 t C6

C5 v ∃r.C3

C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4

C9 v B t C8 t C6

C5 v ∃r.C3

C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3

C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformation

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C u E C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5 u F

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

TU Dresden Foundations of Semantic Web Technologies

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Example: Optimized StructuralTransformationwe still can apply the simplification rules:

C7︷ ︸︸ ︷∀r.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C3

u A u ∃s.(

C1︷ ︸︸ ︷A u D)︸ ︷︷ ︸C4

v B t (

C8︷ ︸︸ ︷∃r.(

C2︷ ︸︸ ︷C u E)︸ ︷︷ ︸C5

uF) t ∀r.A︸︷︷︸C6︸ ︷︷ ︸

C9

A u D v C1 C6 v ∀r.AC2 v C C3 u A u C4 v C7

∀r.C1 v C3 C8 v C5

∃s.C1 v C4 C9 v B t C8 t C6

C5 v ∃r.C3 C7 v C9

C2 v E C8 v FTU Dresden Foundations of Semantic Web Technologies

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Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B

A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)

A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B

A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B

r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B

→ (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Translation into Clausesa TBox with simplified axioms can now be translated into clauses (written asrules):

A1 u . . . u An v B1 t . . . t Bm

A1(x) ∧ . . . ∧ An(x)→ B1(x) ∨ . . . ∨ Bm(x)

If m = 0, the rule head contains ⊥(x).

A v ∃r.B A(x)→ (∃r.B)(x)A v ∀r.B A(x) ∧ r(x, y)→ B(y)

∃r.A v B r(x, y) ∧ A(y)→ B(x)

∀r.A v B → (∃r.¬A)(x) ∨ B(x)

TU Dresden Foundations of Semantic Web Technologies

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Agenda

• Motivation• Recap: Translation into FOL• Structural Transformation• Translation into Clauses• Summary

TU Dresden Foundations of Semantic Web Technologies

Page 118: Foundations of Semantic Web Technologies - Hypertableau I€¦ · TU Dresden Foundations of Semantic Web Technologies. Example Standard Tableau C T= 8r.(:A) tA a 0 b 1a 1 b 2 nb an

Summary

• axioms can be simplified via structural transformation• simplified axioms can be expressed as rules• existential quantification in rule heads allowed• often these rules allow to avoid nondeterminism

TU Dresden Foundations of Semantic Web Technologies