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Ontology Evolution Under Semantic Constraints. Bernardo Cuenca Grau , Ernesto Jiménez-Ruiz Computer Science Department, University of Oxford Evgeny Kharlamov, Dmitriy Zheleznyakov KRDB research centre , Free University of Bozen -Bolzano KR 2012, Rome. Ontologies: schema + data.

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ontology evolution under semantic constraints

Ontology EvolutionUnder Semantic Constraints

Bernardo Cuenca Grau, Ernesto Jiménez-RuizComputer Science Department, University of Oxford

Evgeny Kharlamov, DmitriyZheleznyakovKRDB research centre, Free University of Bozen-Bolzano

KR 2012, Rome

ontologies schema data
Ontologies: schema + data
  • Schema provide
    • standard vocabularies for data
      • classes (concepts)
      • properties (roles)
    • a way to structure data
    • means for machines to be able to understand data
  • Data is a collections of facts
    • Instantiations of classes
    • Instantiations of properties
domain ontologies
Domain Ontologies
  • Goal: to provide standard vocabularies to communities
  • Clinical sciencesontologies:
    • SNOMED CT: Systematized Nomenclature of Medicine - Clinical Terms
      • > 311k concepts
    • NCIt: National CancerInstitute thesaurus
      • ~ 89k concepts, 200m cross links between them [NCI]
    • FMA: Foundational Model of Anatomy
      • 75k classes, 168k relations, 120k terms, 3.1m relat. inst.
evolution of domain ontologies
Evolution of Domain Ontologies
  • Evolution of SNOMED:
    • 5 geographically distributed teams making modifications
    • every 2 weeks the main team integrates changes, resolves conflicts
    • from 2002 to 2008 SNOMED went from 278k to 311k concepts [SM-1]
  • Evolution of NCIt:
    • 20 full time editors for NCI
    • Developers of NCI do over 900 monthly changes [HKR’08]
  • Evolution of FMA:
    • FMA “is an evolving computer-based knowledge source ...” [FMA]
evolution of domain ontologies1
Evolution of Domain Ontologies
  • At the high level ontologies are changed by
    • addition of information
      • usually referred as revision or update
    • deletion of information
      • usually referred as contraction
  • Evolution may affect both
    • schema level
    • data level
  • A natural requirement: principle of minimal change;

changes should minimally affect ontology

      • structure
      • semantics
languages for domain o ntologies
Languages for Domain Ontologies
  • Evolution of ontologies is a classical problem in KR
    • intensively studied for propositional logic
      • there are different semantics for evolution
      • many complexity results
    • very few results beyond “propositional paradise”
  • Ontology Web Language: OWL 2 – W3C standard
    • OWL 2 (based on SROIQ)
    • OWL 2 QL (based on DL-Lite)
    • OWL 2 EL (based on EL, EL++)
      • e.g. SNOMED

these are not propositional

outline
Outline
  • Existing approaches to evolution
    • Syntactic approach
    • Deductive approach
  • Our approach: evolution under constraints
  • Conclusion & directions
sa evolution process
SA: Evolution Process
  • add/delete
  • minimal change(syntactic)

processing

  • ontologyin L
  • evolvedontologyin L
  • operator
  • newinfo

either axioms to add

or axioms to delete

E.g., contraction operator: takes a maximal subset (w.r.t. set inclusion) of the original ontology which does not entail axioms to be deleted

syntactic approach to evolution
Syntactic Approach to Evolution
  • In the ontology:
    • “Oenophiles are gourmets”
    • “Oenophiles are not koalas”
  • To delete: “Oenophiles are gourmets”
  • To this end it is enough to delete

[HS’05]

[KPSCG’06]

[JRGHB’11]

and

  • In the resulted ontology:
    • “Oenophiles are not gourmets”
    • “Oenophiles are not koalas” is lost

OK

Not desirable

outline1
Outline
  • Existing approaches to evolution
    • Syntactic approach
    • Deductive approach
  • Our approach: evolution under constraints
  • Conclusion & directions
da evolution process
DA: Evolution Process
  • add/delete
  • minimal change

represent

expand

processing

processing

  • evolvedclosurein L
  • ontologyin L
  • closurein L
  • evolvedontologyin L
  • operator
  • newinfo

either axioms to add

or axioms to delete

E.g., contraction operator: takes a maximal subset (w.r.t. set inclusion)of the ontology deductive closure which does not entail axioms to be deleted

what is known
What Is Known?
  • DA have been studied for propositional logic
    • WIDTIO
    • Cross-product
  • What about ontologies?
  • Practical extensions of SA to preserve certain inference
    • [JRGHB’11] implemented in ContentCVS
    • “Manchester” grammar [GPS’12]: extension of [JRGHB’11]with combination of sub-concepts of the ontology

axiom  A ⊑ B | A ⊑ ¬ B | A ⊑ ∃ R.B | A ⊑ ∀ R.B

syntactic approach to evolution1
Syntactic Approach to Evolution
  • In the resulted ontology:
    • “Oenophiles are not gourmets”
    • “Oenophiles are not koalas” is lost
  • ContentCVS & “Manchester” grammar allow to restore the missing disjointness

OK

Not desirable

outline2
Outline
  • Existing approaches to evolution
    • Syntactic approach
    • Deductive approach
  • Our approach: evolution under constraints
  • Conclusion & directions
our proposal in a nutshell
Our Proposal in a Nutshell
  • Generalization of SA and DA under a common framework
  • Our view of principle of minimal change
    • maximize preservation of ontology structure
    • maximize preservation of ontology entailments
  • Preservation language (LP) tells us which class of entailments should be maximized
  • ContentCVS & “Manchester grammar” are instantiations for particular “finite” LP
evolution process
Evolution Process
  • add/delete
  • minimal change

represent

expand

processing

  • sub-ontologyin L
  • sub-ontologyin LO
  • evolvedclosurein LP
  • evolvedclosurein L
  • ontologyin LI
  • closurein LP
  • ontologyin L
  • closurein L
  • evolvedontologyin L
  • evolvedontologyin LO
  • operator
  • newinfo

either axioms to add

or axioms to delete

evolution under semantic constraints
Evolution under Semantic Constraints

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (C+,C-)
  • C+: axioms that should be present in the result
  • C−: axioms that should be absent in the result
  • General evolution encompasses both
    • contraction via C−
    • revision via C+
example contraction
Example: Contraction

processing

  • sub-ontologyin LI
  • Task: delete an axiom A1 ⊑ A2 from an LI-ontology K
  • LO-ontology K’is a contraction of an LI-ontology Kw.r.t. A1 ⊑ A2 if:
    • K’⊭ A1 ⊑ A2
    • K⊨ K’
  • Contraction may not be optimal
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (∅,C-)

LI: input lang.

LO: output lang.

LP: preservation lang.

example contraction1
Example: Contraction

processing

  • sub-ontologyin LI
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (∅,C-)
  • newdta

LI: input lang.

LO: output lang.

LP: preservation lang.

  • Task: delete an axiom A1 ⊑ A2 from and LI-ontology K
  • AcontractionK’ of K isoptimalw.r.t. LP if it maximally preserves:
    • structure of KK’ ∩ K⊄ K’’∩ Kfor every contr. K’’
    • LP-entailments of Knot true: if K’⊨α then K’’⊨α for every contr. K’’
example contraction2
Example: Contraction

Delete: Gourmet ⊑ French

Contraction

Optimal

Contraction

Optimal

evolution with finite lp
Evolution with Finite LP

processing

  • sub-ontologyin LO
  • Ontology language Lover a finite signatureΣisfinite if there are finitely many non-equivalent L-formulas over Σ
  • Examples of LP:
    • ContentCVS &”Manchester” grammars finite
    • OWL 2 QL (DL-Lite) finite
    • OWL 2 EL (EL, EL++, FL0) infinite
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (C+,C–)
evolution with finite lp1
Evolution with Finite LP

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (C+,C–)

always exists

finite LP

Theorem: If the preservation language LP is finite, then

  • an optimal evolution always exists (provided an evolution exists)
  • both O and LP-closure of O are finite  we can simply write the result
evolution with infinite lp
Evolution with Infinite LP

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (C+,C–)

finite representationmay not exist

infinite LP

  • What if LP is infinite?
  • We have a problem!
  • Optimal evolution may not exist!
evolution with infinite lp1
Evolution with Infinite LP

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (C+,C–)

FL0

EL

FL0

EL

EL

FL0

Theorem:

  • If FL0 setting optimal evolution does not exist in general
  • If EL setting optimal evolution does not exist in general
    • complex interaction of cycles and recursions
infinite lp exponential case
Infinite LP: Exponential Case

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (∅,C-)

acyclic EL

acyclic EL

chain EL

cyclic

  • ChainEL consists of inclusion assertions
    • A1⊑ ∃R1…Rn.A2or
    • ∃R1…Rn.A1 ⊑ A2
  • It is a simple infinite language to study expressibility
  • An acyclic ontology has acyclic canonical model
  • SNOMED and NCItare acyclic
  • opt. contraction always exists
  • EXP time computation
infinite lp polynomial case
Infinite LP: Polynomial Case

processing

  • sub-ontologyin LO
  • ontologyin LI
  • closurein LP
  • operator
  • evolvedontologyin LO
  • evolvedclosurein LP
  • semanticconstraints
  • (∅,C-)

non-rec EL

non-rec EL

chain EL

non-recursive

  • An ontology is non-recursiveif concepts of the form ∃R.C donot appear at the left-hand side of axioms
  • Simplest non-recursive EL sub-language
  • opt. contraction always exists
  • PTimecomputation
summary
Summary

ruled out by LP

outline3
Outline
  • Existing approaches to evolution
    • Syntactic approach
    • Deductive approach
  • Our approach: evolution under constraints
  • Conclusion & directions
conclusion directions
Conclusion & Directions
  • We introduced SDA:
  • a novel framework for ontology evolution
  • SDA generalizes:
    • syntactic approaches and
    • deductive approaches
  • it provides flexible means to navigate between SA and DA

We studied 4 settings for SDA:

  • Directions:
  • extend the current results to richer LP: chain EL  ?
    • evolution beyond EL
references
References
  • [HKR’08] Hartung, M.; Kirsten, T.; and Rahm, E. 2008. Analyzing the evolution of life science ontologies and mappings. In Proc. of DILS, 11–27.
  • [SM-1] http://www.ihtsdo.org/snomed-ct/snomed-ct0/adoption-of-snomed-ct/
  • [FMA] http://sig.biostr.washington.edu/projects/fm/AboutFM.html
  • [NCI] https://wiki.nci.nih.gov/display/EVS/NCI+Thesaurus+versus+NCI+Metathesaurus
  • [HS’05]Haase, P., Stojanovic, L.: Consistent evolution of OWL ontologies. In: ESWC. (2005)
  • [KPSCG’06] Kalyanpur, A., Parsia, B., Sirin, E., Grau, B.C.: Repairingunsatisfiableconcepts in OWL ontologies. In: ESWC. (2006) 170–184
  • [JRCGHB’11] Jimenez-Ruiz, E., Cuenca Grau, B., Horrocks, I., Berlanga, R.: Supporting concurrent ontology development: Framework, algorithms and tool. DKE. 70:1 (2011)
  • [GPS’12]: Rafael S. Gonçalves, BijanParsia, Ulrike Sattler. 2012. Concept-based semantic difference in expressive description logics. In Proc. of DL
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