computing stable models for nonmonotonic existential rules

COMPUTING STABLE MODELS FOR

NONMONOTONIC EXISTENTIAL RULES

Despoina Magka, Markus Krötzsch, Ian Horrocks

Department of Computer Science, University of Oxford

IJCAI, 2013

THE OWLS ARE NOT WHAT THEY SEEM

OWL widely used for authoring biomedical ontologies

Not marked for its ability to model cyclic structures

Such structures abound in life science (and other) domains

hasParticipant

locatedIn

reactantproduct

transportReaction 2-ketoglutarate-malate-antiport

2-ketoglutarate malate

mitochondrion

cytosol

Rules with nonmonotonic negation in the body andexistentials in the head

B1 ∧ . . . ∧ Bn ∧ not Bn+1 ∧ . . . ∧ not Bm → ∃y.H1 ∧ . . . ∧ Hk

Interpreted under stable model semantics

Good for representing non-tree-shaped structures

Existentials allow us to infer new structures

Nonmonotonicity adds extra expressivity in modelling

Stable model semantics supported by many tools: DLV,clasp, . . .

CLASSIFICATION OF STRUCTURED OBJECTS I

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

methanol(x)→ ∃6i=1yi. ∧6

i=1 hasAtom(x, yi) ∧ c(y1) ∧ o(y2) ∧∧6

i=3h(yi)∧ ∧5i=2 bond(y1, yi) ∧ bond(y2, y6)

∧3i=1 hasAtom(x, zi) ∧ c(z1)∧o(z2) ∧

h(z3) ∧ bond(z1, z2) ∧ bond(z2, z3)→ organicHydroxy(x)

hasAtom(x, z) ∧ o(z)→ hasOxygen(x)

methanol v organicHydroxy 3 methanol v hasOxygen 3

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

methanol v organicHydroxy 3

methanol v hasOxygen 3

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

Methanol molecule

hasAtombond

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

CLASSIFICATION OF STRUCTURED OBJECTS II

Organic hydroxy group

hasAtombond

horganicHydroxy

g2(h) g3(h)

hasOxygen

organicHydroxy(x)→ ∃3i=1yi. ∧3

i=1 hasAtom(x, yi) ∧ c(y1)

∧ o(y2) ∧ h(y3) ∧ bond(y1, y2)

∧ bond(y2, y3)

organicHydroxy v hasOxygen 3

hasAtombond

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ o(y2) ∧ h(y3) ∧ bond(y1, y2)

∧ bond(y2, y3)

hasAtombond

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ o(y2) ∧ h(y3) ∧ bond(y1, y2)

∧ bond(y2, y3)

hasAtombond

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ o(y2) ∧ h(y3) ∧ bond(y1, y2)

∧ bond(y2, y3)

hasAtombond

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ o(y2) ∧ h(y3) ∧ bond(y1, y2)

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

i=1 hasAtom(x, yi) ∧ . . .

∧ bond(y2, y6)∧3

i=1 hasAtom(x, zi) ∧ . . .∧bond(z2, z3)→ organicHydroxy(x)

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

INCORRECT MODELLING

mmethanol

f2(m) f3(m)

f6(m)g1(m)

g2(m) g3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

methanol v hasOneCarbon 8

∧ bond(y2, y6)∧3

∧ bond(y2, y3)

REPAIR WITH AUXILIARY PREDICATES

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

i=1 hasAtom(x, zi) ∧ . . .∧bond(z2, z3) ∧ not gh(z1)

∧ not gh(z2) ∧ not gh(z3)→ organicHydroxy(x) ∧ rh(x)

organicHydroxy(x)∧ not rh(x)→ ∃3i=1yi. ∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

hasAtom(x, z) ∧ o(z)→ hasOxygen(x)6

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

WHAT’S THE PROBLEM?

Reasoning is undecidable(even fact entailment, even without not )

many known conditions for regaining decidabilityacyclicity conditions ensure finite models: (super)-weakacyclicity, joint acyclicity, aGRD, MSA, MFA, . . .

Reasoning is hard (even for finite models)

stable models lead to non-determinismstratification conditions ensure determinism

Stratification

Stable model uniqueness

Deterministic reasoning

Stratification

Reasoning is hard (even for finite models)stable models lead to non-determinismstratification conditions ensure determinism

Stratification

Reasoning is hard (even for finite models)stable models lead to non-determinismstratification conditions ensure determinism

Stratification

WHAT’S OUR PROBLEM?

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

Repaired program not stratified

∧ bond(y2, y6)∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

mmethanol

f2(m) f3(m)

organicHydroxy

hasOxygen

horganicHydroxy

g2(h) g3(h)

hasOxygen

∧ bond(y2, y6)∧3

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

RESULTS OVERVIEW

1 R-acyclicity and R-stratification conditionsR-stratification ensures stable model uniquenessBoth coNP-complete to check

2 Complexity of reasoning

Fact entailment Program comp. Data comp.R-acyclic coN2ExpTime-complete coNP-completeR-acyclic+R-stratified 2ExpTime-complete PTime-complete

3 Generalise R-acyclicity and R-stratification with constraints new conditions ΠP

2 -complete to check4 Experiments over ChEBI with DLV

Performance gains in DLV using R-stratificationMissing subsumptions from ChEBI ontology

RESULTS OVERVIEW

2 -complete to check

4 Experiments over ChEBI with DLVPerformance gains in DLV using R-stratificationMissing subsumptions from ChEBI ontology

RESULTS OVERVIEW

POSITIVE RELIANCES

Rule r2 positively relies on r1 (written r1+−→ r2): there is a

situation when r1 can trigger r2 to derive something new

EXAMPLE

r1 : ∧3i=1 hasAtom(x, zi) ∧

c(z1) ∧ o(z2) ∧ h(z3) ∧bond(z1, z2) ∧ bond(z2, z3)→ organicHydroxy(x)

r2 : organicHydroxy(x)→ ∃3i=1yi. ∧3

i=1 hasAtom(x, yi) ∧c(y1) ∧ o(y2) ∧ h(y3) ∧bond(y1, y2) ∧ bond(y2, y3)

r1+−→ r2 but r2 6

+−→ r1

NP-complete to check(but only w.r.t. the size of the rules)

POSITIVE RELIANCES

EXAMPLE

r1+−→ r2 but r2 6

+−→ r1

POSITIVE RELIANCES

EXAMPLE

r1+−→ r2

but r2 6+−→ r1

POSITIVE RELIANCES

EXAMPLE

r1+−→ r2 but r2 6

+−→ r1

POSITIVE RELIANCES

EXAMPLE

r1+−→ r2 but r2 6

+−→ r1

POSITIVE RELIANCES

EXAMPLE

r1+−→ r2 but r2 6

+−→ r1

R-ACYCLICITY

A program is R-acyclic: there is no cycle of positivereliances that involves a rule with an existential

Checking R-acyclicity is coNP-complete

Similar to ≺-stratification [Deutsch et al., PODS, 2008];extension of aGRD [Baget et al., RR, 2011]

Fact entailment for R-acyclic programs

Stable models bounded in size (double exp),but many models possible

coN2ExpTime-complete w.r.t. program complexity

coNP-complete w.r.t. data complexity

R-ACYCLICITY

NEGATIVE RELIANCES

Rule r2 negatively relies on r1 (written r1−−→ r2): there is a

situation when r1 can inhibit the application of r2

EXAMPLE

r1 : ∧3i=1 hasAtom(x, zi) ∧ c(z1) ∧

o(z2) ∧ h(z3) ∧ bond(z1, z2) ∧bond(z2, z3) ∧ not gh(z1) ∧

not gh(z2) ∧ not gh(z3)→ organicHydroxy(x) ∧ rh(x)

r2 : organicHydroxy(x) ∧ not rh(x)→ ∃3i=1yi. ∧3

i=1 hasAtom(x, yi)

∧ c(y1) ∧ o(y2) ∧ h(y3) ∧bond(y1, y2) ∧ bond(y2, y3)

∧ gh(y1) ∧ gh(y2) ∧ gh(y3)

r1−−→ r2 but r2 6

−−→ r1

Polynomial time to check

NEGATIVE RELIANCES

EXAMPLE

i=1 hasAtom(x, yi)

∧ gh(y1) ∧ gh(y2) ∧ gh(y3)

r1−−→ r2 but r2 6

−−→ r1

NEGATIVE RELIANCES

EXAMPLE

i=1 hasAtom(x, yi)

∧ gh(y1) ∧ gh(y2) ∧ gh(y3)

r1−−→ r2

but r2 6−−→ r1

NEGATIVE RELIANCES

EXAMPLE

i=1 hasAtom(x, yi)

∧ gh(y1) ∧ gh(y2) ∧ gh(y3)

r1−−→ r2 but r2 6

−−→ r1

NEGATIVE RELIANCES

EXAMPLE

i=1 hasAtom(x, yi)

∧ gh(y1) ∧ gh(y2) ∧ gh(y3)

r1−−→ r2 but r2 6

−−→ r1

Polynomial time to check12

R-STRATIFICATION

A program P is R-stratified if there is a partition P1, . . . ,Pn ofP such that for Pi,Pj and rules r1 ∈ Pi and r2 ∈ Pj, we have:

if r1+−→ r2 then i ≤ j and if r1

−−→ r2 then i < j.

Strictly extends ‘classical’ stratification ensures stable model uniquenesscoNP-complete to check

Fact entailment for R-acyclic, R-stratified programs

Stable models bounded in size (double exp),and at most one stable model2ExpTime-complete w.r.t. program complexityPTime-complete w.r.t. data complexity

R-STRATIFICATION

−−→ r2 then i < j.

EXAMPLE

r2r1 ++

r3 r4 r5

S1~P= TP1(F)

S2~P= TP2(S

S3~P= TP3(S

R-STRATIFICATION

−−→ r2 then i < j.

EXAMPLE

r2r1 ++

r3 r4 r5

S1~P= TP1(F)

S2~P= TP2(S

S3~P= TP3(S

R-STRATIFICATION

−−→ r2 then i < j.

EXAMPLE

r2r1 ++

r3 r4 r5

S1~P= TP1(F)

S2~P= TP2(S

S3~P= TP3(S

R-STRATIFICATION

−−→ r2 then i < j.

EXAMPLE

r2r1 ++

r3 r4 r5

S1~P= TP1(F)

S2~P= TP2(S

S3~P= TP3(S

R-STRATIFICATION

−−→ r2 then i < j.

R-STRATIFICATION

−−→ r2 then i < j.

Fact entailment for R-acyclic, R-stratified programsStable models bounded in size (double exp),and at most one stable model

2ExpTime-complete w.r.t. program complexityPTime-complete w.r.t. data complexity

R-STRATIFICATION

−−→ r2 then i < j.

Fact entailment for R-acyclic, R-stratified programsStable models bounded in size (double exp),and at most one stable model2ExpTime-complete w.r.t. program complexity

PTime-complete w.r.t. data complexity

R-STRATIFICATION

−−→ r2 then i < j.

Fact entailment for R-acyclic, R-stratified programsStable models bounded in size (double exp),and at most one stable model2ExpTime-complete w.r.t. program complexityPTime-complete w.r.t. data complexity

RELIANCES UNDER CONSTRAINTS

Restrict input sets of facts to relax R-acyclicity andR-stratification using constraints

EXAMPLE

r1 : mol(x) ∧ hasAtom(x, z) ∧ c(z)→ organic(x)

r2 : mol(x) ∧ not organic(x)→ inorganic(x)

r3 : inorganic(x)→ mol(x) ∧ geoOrigin(x)

C = {inorganic(x) ∧ hasAtom(x, z)

∧ c(z)

→ ⊥}r1−−→ r2

+−→ r3+−→ r1 but r3 6

+−→C r1

Slightly more complex to check:

Positive reliance Negative reliance R-acyclicity/R-stratificationΣP

2 -complete in ∆P2 ΠP

2 -complete

ΣP2 -hardness follows from satisfiability of a QBF ∃~p.∀~q.ϕ

EXAMPLE

∧ c(z)

→ ⊥}r1−−→ r2

+−→ r3+−→ r1 but r3 6

+−→C r1

2 -complete

EXAMPLE

∧ c(z)

→ ⊥}

r1−−→ r2

+−→ r3+−→ r1

but r3 6+−→C r1

2 -complete

EXAMPLE

C = {inorganic(x) ∧ hasAtom(x, z) ∧ c(z)→ ⊥}r1−−→ r2

+−→ r3+−→ r1

but r3 6+−→C r1

2 -complete

EXAMPLE

+−→ r3+−→ r1 but r3 6

+−→C r1

2 -complete

EXAMPLE

+−→ r3+−→ r1 but r3 6

+−→C r1

2 -complete

EXPERIMENTAL SETUP

Chemical Entities of Biological InterestReference terminology adopted for chemical annotation bymajor bio-ontologies~20,000 molecule and ~8,000 chemical class descriptionsChEBI taxonomy manually curated

Our knowledge base consisted of rules derived from ChEBIthat represented

500 molecules30 molecular part descriptions50 chemical class descriptions

78,957 rules in total (R-stratified and R-acyclic)

Used DLV for stable model computation

EXPERIMENTAL SETUP

500 molecules

EXAMPLE

i=1 hasAtom(x, yi) ∧ . . . ∧ bond(y2, y6)

30 molecular part descriptions50 chemical class descriptions

EXPERIMENTAL SETUP

500 molecules30 molecular part descriptions

EXAMPLE

∧3i=1 hasAtom(x, zi) ∧ . . .∧bond(z2, z3) ∧ not gh(z1)∧ not gh(z2) ∧ not gh(z3)→ organicHydroxy(x) ∧ rh(x)

∧ bond(y2, y3) ∧ ∧3i=1gh(yi)

50 chemical class descriptions

EXPERIMENTAL SETUP

EXAMPLE

EXPERIMENTAL SETUP

EMPIRICAL RESULTS

First attempt to compute the stable model of the overallprogram P failed (no result after 600 secs)

Second attempt exploited partition of the program into tworule sets according to R-stratificationComputed 8,639 subclass relations in 13.5 secsRevealed missing subsumptions from the ChEBI ontology

E.g. organicHydroxy v organoOxygenCompound 3

EMPIRICAL RESULTS

First attempt to compute the stable model of the overallprogram P failed (no result after 600 secs)Second attempt exploited partition of the program into tworule sets according to R-stratification

Computed 8,639 subclass relations in 13.5 secsRevealed missing subsumptions from the ChEBI ontology

EMPIRICAL RESULTS

Split into lowest R-stratum P1and remaining four upper R-strata P5

Computed stable model S1~P

of P1 ∪ F

of P52 ∪ S1

EMPIRICAL RESULTS

of P1 ∪ F

of P52 ∪ S1

EMPIRICAL RESULTS

of P1 ∪ F

of P52 ∪ S1

EMPIRICAL RESULTS

First attempt to compute the stable model of the overallprogram P failed (no result after 600 secs)Second attempt exploited partition of the program into tworule sets according to R-stratificationComputed 8,639 subclass relations in 13.5 secs

Revealed missing subsumptions from the ChEBI ontology

EMPIRICAL RESULTS

First attempt to compute the stable model of the overallprogram P failed (no result after 600 secs)Second attempt exploited partition of the program into tworule sets according to R-stratificationComputed 8,639 subclass relations in 13.5 secsRevealed missing subsumptions from the ChEBI ontology

EMPIRICAL RESULTS

First attempt to compute the stable model of the overallprogram P failed (no result after 600 secs)Second attempt exploited partition of the program into tworule sets according to R-stratificationComputed 8,639 subclass relations in 13.5 secsRevealed missing subsumptions from the ChEBI ontology

CONCLUSIONS

R-acyclicity and R-stratification conditions(coNP-complete to check)

Generalise with constraints (ΠP2 -complete to check)

Performance gains in DLV & new subsumptions in ChEBI

Future directions:More general notions of ‘rule’ + equality in rule heads[LPNMR’13]Compare performance with other ASP solvers[chemical classification problem, ASPCOMP’13]

Thank you! Questions?!?

CONCLUSIONS

Future directions:More general notions of ‘rule’ + equality in rule heads[LPNMR’13]

Compare performance with other ASP solvers[chemical classification problem, ASPCOMP’13]

CONCLUSIONS

computing stable models for nonmonotonic existential rules

Technology

artificial nonmonotonic neural networks

ilp 2014 - nonmonotonic learning in large biological network

modeling nonmonotonic dose-response curves

existential psychotherapy

existential analysis 15.2: july 2004 on existential...

existential elimination

scalable nonmonotonic reasoning over rdf data using ...

inverse entailment in nonmonotonic logic programs

nonmonotonic causal theories - university of minnesota...

witold l ukaszewicz: nonmonotonic reasoning: formaliza...

introduction to nonmonotonic reasoning

comp4418: knowledge representation and reasoning -...

existential consistency:measuring and understanding...

nonmonotonic variation with salt concentration of the second

new nonmonotonic plasticity: how memory retrieval drives...

existential therapy

logical representation of preference & nonmonotonic...

radical behaviourism rft existential- phenomenological...

nonmonotonic reasoning -...

heterogeneous drying and nonmonotonic contact angle