Ontology Ontology Translation for the Translation for the

Semantic WebSemantic Web byby

Dejing Don, Drew McDermott, and Peishen QiDejing Don, Drew McDermott, and Peishen Qi

The Problem SetThe Problem Set

Ontology TranslationOntology Translation Dataset Translation*Dataset Translation* Ontology Extension TranslationOntology Extension Translation Querying through different Querying through different

OntologiesOntologies- “- “ontology translation by ontology ontology translation by ontology merging and automated reasoning.”merging and automated reasoning.”

AssumptionsAssumptions Ignoring syntatic differencesIgnoring syntatic differences A merged ontology is formed by:A merged ontology is formed by:

– the union of the terms and axioms the union of the terms and axioms of two ontologiesof two ontologies

– an extension of said axioms to an extension of said axioms to bridge concepts from one ontology bridge concepts from one ontology to the other (called to the other (called bridging axioms)bridging axioms)

Software InvolvedSoftware Involved OntoEngineOntoEngine

– Involved with all three of tasks in the Involved with all three of tasks in the problem setproblem set

OntoMergeOntoMerge– Semi-automated (machine assisted expert Semi-automated (machine assisted expert

tool) tool) – Online nexusOnline nexus

PDDAML PDDAML – Web-PDDLWeb-PDDL

Test Set OneTest Set One Yale vs. CMUYale vs. CMU

Yale (Yale (yale_bib ontologyyale_bib ontology))

CMU(CMU(cmu_bib onotologycmu_bib onotology))

ArticleArticle InproceedingsInproceedings IncollectionIncollection

ArticleArticle

Test Set TwoTest Set Two

Booktitle

Collection Proceedings

Incollection(String)

Inproceedings(String)

Booktitle(string)

Yale (yale_bib) CMU (cmu_bib)

Test Set ThreeTest Set Three European Geneaology OntologiesEuropean Geneaology Ontologies

bbn_ged vs. drc_gedbbn_ged vs. drc_ged

Translation vs. Translation vs. MappingMapping

As defined by this paper:As defined by this paper:Mapping:Mapping: finding or mapping finding or mapping

the relationships between two the relationships between two ontologies.ontologies.

Translation:Translation: given a set of given a set of facts in one vocabulary (the facts in one vocabulary (the sourcesource), infer the largest possible ), infer the largest possible set of consquences in another (the set of consquences in another (the targettarget).).

Ontology MappingOntology Mapping still human dependantstill human dependant machine learning mapping machine learning mapping

– Saves timeSaves time– SuggestionsSuggestions

Ontology translators can’t use Ontology translators can’t use automated ontology mappers:automated ontology mappers:– AccuracyAccuracy– ComplexityComplexity

Translation ChoicesTranslation Choices

Global all-encompassing One True Theory ontology that covers all existing ontologies. All that wish to be apart of the Semantic Web must write translations from their ontologies to the One True Theory. (Ontolingua)

The other strategy is to do on-the-fly ontology translation directly from a dataset in a (source) ontology to a dataset in another (target) ontology, on a dataset-by-dataset basis. (OntoMorph)

ApproachApproach 3 Parts:3 Parts:

i.i. Syntatic translation parseSyntatic translation parseii.ii. Semantic translationSemantic translationiii.iii. Syntatic translation outputSyntatic translation output

PDDAML PDDAML - translator- translatorthe translator for parts one and three the translator for parts one and three built to be upgradable (originally built built to be upgradable (originally built for DAML+OIL, can now handle OWL)for DAML+OIL, can now handle OWL)internal language Web-PDDLinternal language Web-PDDL

Web-PDDLWeb-PDDL Internal representation used by OntoEngine to Internal representation used by OntoEngine to

make semantic inferences.make semantic inferences. Extends PDDL with XML namespaces and Extends PDDL with XML namespaces and

additional axiom notation.additional axiom notation.(define (domain yale_bib-ont)(:extends (uri "http://www.w3.org/2000/01/rdf-

schema#":prefix rdfs))(:types Publication – Obj

Article Book Incollection Inproceedings - Publication

Literal - @rdfs:Literal)(:predicates (author p - Publication a - Literal).....))

Putting it all togetherPutting it all together

Translation -> from source vocabulary to target vocbulary:

Inference & Projection

Sentence : “The publication BretonZucker96 appeared in the Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition"

In yale_bib:(:objects ... BretonZucker96 - InProceedings)(:facts...(booktitle BretonZucker96 "Proceedings of CVPR'96"))

In cmu_bib:(:objects ... BretonZucker96 - Article proc38 - Proceedings)(facts ... (inProceedings BretonZucker96 proc38)(booktitle proc38 "Proceedings of CVPR'96") ...)

Bridging Axioms: to relate yale_bib & cmu_bib(forall (a - Article tl - String)(iff (@yale_bib:booktitle a tl) (booktitle a tl)))(forall (a - @yale_bib:Inproceedings tl - String)(iff (booktitle a tl)(exists (p - Proceedings)(and (contain p a)(@cmu_bib:inProceedings a p)(@cmu_bib:booktitle p tl)))))

Existentialism in Code(forall (a - @yale_bib:Inproceedings tl - String)

(iff (booktitle a tl)(exists (p - Proceedings)(and (contain p a)(@cmu_bib:inProceedings a p)(@cmu_bib:booktitle p tl)))))

p - existential modifier p - existential modifier Skolem term?:Skolem term?:

Skolem finalized set theory axoimsSkolem finalized set theory axoims Another Skolem theory about countably infinite Another Skolem theory about countably infinite

subset N of larger set M for which N satisfies all subset N of larger set M for which N satisfies all ‘first-order’ sentences accepted by M.‘first-order’ sentences accepted by M.

Theorem Proving Theorem Proving OntoEngineOntoEngine ConcernsConcerns Necessary inferencesNecessary inferences

– Forward chaining from source to target ontologies.– Backward chaining for queries in one ontology to

datasets in another.– Introduction of skolem terms and term-generating

functions as explained above.– Use of equalities to substitute existing constant

terms for skolem terms.

OntoEngine Features Stops:(protection against theorem

loops)• Hard coded limit to complexity of terms

that OntoEngine can generate • Deductive Engines stops when it reaches

conclusions or goals, in the case of backward chaining, in the target ontology.

Good type-checking system making use of the strong typed feature of Web-PDDL

Translation Completeness: traded completeness for efficiency

“Anything that can be expressed in the source ontology can be expressed in the target ontology.”

Experiment 2Experiment 2bbn_ged -> 21164 facts bbn_ged -> 21164 facts (3010 individuals & 1422 (3010 individuals & 1422

families)families)(@bbn_ged:name @royal92:@I1248@ "Francis_II")(@bbn_ged:sex @royal92:@I1248@ "M")(@bbn_ged:spouseIn @royal92:@I1248@ @royal92:@F456@)(@bbn_ged:marriage @royal92:@F456 @royal92:event3138)(@bbn_ged:date @royal92:event3138 "24 APR 1558")(@bbn_ged:place @royal92:event3138 "Paris,France")

drc_ged -> 26596 factsdrc_ged -> 26596 factsinstead of spouseIn, has husband and wife. bbn_ged infers from instead of spouseIn, has husband and wife. bbn_ged infers from marriage and gender.marriage and gender.

mapmap(forall (f - Family h - Individual m - Marriage)

(if (and (@bbn_ged:sex h "M") (@bbn_ged:spouseIn h f)(@bbn_ged:marriage f m))

(husband f h))…)

Exp 2 ResultsExp 2 Results 85555 reasoning steps to generate all 85555 reasoning steps to generate all

26956 facts @ 59 seconds.26956 facts @ 59 seconds.http://cs-www.cs.yale.edu/homes/dvm/daml/ontology-translation.html

(@bbn_ged:name @royal92:@I1248@ "Francis_II")(@bbn_ged:sex @royal92:@I1248@ "M")(@bbn_ged:spouseIn @royal92:@I1248@ @royal92:@F456@)(@bbn_ged:marriage @royal92:@F456 @royal92:event3138)(@bbn_ged:date @royal92:event3138 "24 APR 1558")(@bbn_ged:place @royal92:event3138 "Paris,France")(@drc_ged:name @royal92:@I1248@ "Francis_II")(@drc_ged:sex @royal92:@I1248@ "M")(@drc_ged:husband @royal92:@F456 @royal92:@I1248@)(@drc_ged:marriage @royal92:@F456 @royal92:event3138)(@drc_ged:date @royal92:event3138 "24 APR 1558")(@drc_ged:location @royal92:event3138 "Paris,France")

Ontology Extension Ontology Extension GenerationGeneration

If we know the relationships of existing ontologies (A & If we know the relationships of existing ontologies (A & B) and we want to generate subonotolgies of those, B) and we want to generate subonotolgies of those, what inferences can we assume about the relationships what inferences can we assume about the relationships between our created subontologies (c & d). between our created subontologies (c & d).

Automatic Updates (propagating changes to Automatic Updates (propagating changes to other levels of ontologies) in PDDAMLother levels of ontologies) in PDDAML

Example provided using Congo’s delivery Example provided using Congo’s delivery system, in which they automatically created a fairly system, in which they automatically created a fairly close match to what their experts created manually.close match to what their experts created manually.

Problems Future Work Problems Future Work w/ Ontology Extensionw/ Ontology ExtensionWhen generating ontology When generating ontology extensions, PDDAML can translate extensions, PDDAML can translate the types, predicates and only the types, predicates and only those axioms that are sub-those axioms that are sub-properties of Oproperties of Os1s1 to corresponding to corresponding properties in Oproperties in Os2s2 and not general and not general axioms.axioms.

Querying through Querying through different ontologiesdifferent ontologies

Example: Genealogy drc_ged Find the name Example: Genealogy drc_ged Find the name of King Henry VI’s that married him on the of King Henry VI’s that married him on the date given in the previous example.date given in the previous example.

Find those ontologies that can accept partial Find those ontologies that can accept partial drc_ged ontology queries that help us answer drc_ged ontology queries that help us answer our question.our question.(:query (freevars (?k ?q - Individual ?f - Family ?m - Marriage ?n - @xsd:string ?d - @xsd:date) (and (@drc_ged:name ?k "Henry_VI") (@drc_ged:husband ?f ?k) (@drc_ged:wife ?f ?q) (@drc_ged:name ?q ?n) (@drc_ged:marriage ?f ?m) (@drc_ged:date ?m ?d))))

(@drc ged:name ?k "Henry VI") (@drc ged:name ?k "Henry VI") (@bbn_ged:name ?k "Henry VI")(@bbn_ged:name ?k "Henry VI") {?k/@royal92:@I1217@} {?k/@royal92:@I1217@} (@drc_ged:husband?f @royal92: @I1217@) (@drc_ged:husband?f @royal92: @I1217@) {?f/ @royal92:@F448@}{?f/ @royal92:@F448@} (and (@drc_ged:wife royal92:@F448@ ?q)(and (@drc_ged:wife royal92:@F448@ ?q)

(@drc_ged:marriage@royal92:@F448@?m))(@drc_ged:marriage@royal92:@F448@?m)) (and (@bbn_ged:sex ?q "F") (@bbn_ged:spouseIn (and (@bbn_ged:sex ?q "F") (@bbn_ged:spouseIn

?q @royal92:@F448@)?q @royal92:@F448@) (@bbn_ged:marriage @royal92:@F448@ ?m))(@bbn_ged:marriage @royal92:@F448@ ?m)) The bindings this time are The bindings this time are

{?q/@royal92:@I1218@}, and {?q/@royal92:@I1218@}, and {?m/@royal92:event3732}.{?m/@royal92:event3732}.

(and (@bbn_ged:name @royal92:@I1218@ ?n)(and (@bbn_ged:name @royal92:@I1218@ ?n)(@bbn_ged:date @royal92:event3732 ?d))(@bbn_ged:date @royal92:event3732 ?d))

The ultimate result is {?n/"Margaret of Anjou"} The ultimate result is {?n/"Margaret of Anjou"} and {?d/"22 APR 1445"}.and {?d/"22 APR 1445"}.

Backward QueryingBackward Querying Incomplete tool, a lot of research Incomplete tool, a lot of research

being done in this area.being done in this area. Not the focus of this paperNot the focus of this paper

Related WorkRelated Work This work relies on inference through This work relies on inference through

bridging axioms and layering logic on top bridging axioms and layering logic on top of RDF to get free of relying on of RDF to get free of relying on descriptions.descriptions.

Prompt & Chimera- name similarity and Prompt & Chimera- name similarity and taxonomic relation matchingtaxonomic relation matching

GLUE- generates only simple mapping rulesGLUE- generates only simple mapping rules Incremental Ontology building – more Incremental Ontology building – more

simple rules, but more complicated simple rules, but more complicated algorthims.algorthims.

ConclusionsConclusions Ontology translationOntology translation

Translating datasetsTranslating datasets Generating Ontology ExtensionsGenerating Ontology Extensions Querying through different OntologiesQuerying through different Ontologies

Ontology translation thought of as Ontology translation thought of as Ontology merging.Ontology merging.

If all ontologies, datasets and queries If all ontologies, datasets and queries can be expressed in the same terms, can be expressed in the same terms, semantic translation can be automatic.semantic translation can be automatic.

OntoMerge – Ontology translation OntoMerge – Ontology translation server.server.

New avenues for automating New avenues for automating production of bridging axioms.production of bridging axioms.

Future DevelopmentFuture Development Tools to validate consistency of Tools to validate consistency of

generated bridging axioms.generated bridging axioms. Tools to help experts build axioms Tools to help experts build axioms

through dialogues about the form of through dialogues about the form of the desired relation between the desired relation between ontologies in question.ontologies in question.