1. Ontology Translation for the Semantic Web by by Dejing Don, Drew McDermott, and Peishen Qi Dejing Don, Drew McDermott, and Peishen Qi

2. The Problem Set Ontology Translation  Dataset Translation*  Ontology Extension Translation  Querying through different Ontologies - “ontology translation by ontology merging and automated reasoning.”

3. Assumptions Ignoring syntatic differences Ignoring syntatic differences A merged ontology is formed by: A merged ontology is formed by: – the union of the terms and axioms of two ontologies –an extension of said axioms to bridge concepts from one ontology to the other (called bridging axioms)

4. Software Involved OntoEngine OntoEngine –Involved with all three of tasks in the problem set OntoMerge OntoMerge –Semi-automated (machine assisted expert tool) –Online nexus PDDAML PDDAML –Web-PDDL

5. Test Set One Yale vs. CMU Yale vs. CMU Yale (yale_bib ontology) CMU(cmu_bib onotology) ArticleInproceedingsIncollection Article

6. Test Set Two Booktitle Collection Incollection (String) Proceedings Inproceedings (String) Booktitle (string) Yale (yale_bib) CMU (cmu_bib)

7. Test Set Three European Geneaology Ontologies European Geneaology Ontologies bbn_ged vs. drc_ged

8. Translation vs. Mapping As defined by this paper: Mapping: finding or mapping the relationships between two ontologies. Translation: given a set of facts in one vocabulary (the source), infer the largest possible set of consquences in another (the target).

9. Ontology Mapping still human dependant still human dependant machine learning mapping machine learning mapping –Saves time –Suggestions Ontology translators can’t use automated ontology mappers: Ontology translators can’t use automated ontology mappers: –Accuracy –Complexity

10. Translation 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)

11. Approach 3 Parts: 3 Parts: i.Syntatic translation parse ii.Semantic translation iii.Syntatic translation output PDDAML - translator the translator for parts one and three built to be upgradable (originally built for DAML+OIL, can now handle OWL) internal language Web-PDDL

12. Web-PDDL Internal representation used by OntoEngine to make semantic inferences. Internal representation used by OntoEngine to make semantic inferences. Extends PDDL with XML namespaces and additional axiom notation. Extends PDDL with XML namespaces and 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).....))

13. Putting it all together Translation -> from source vocabulary to target vocbulary: Inference & Projection

14. 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)))))

15. 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 axoims Another Skolem theory about countably infinite subset N of larger set M for which N satisfies all ‘first-order’ sentences accepted by M. Another Skolem theory about countably infinite subset N of larger set M for which N satisfies all ‘first-order’ sentences accepted by M.

16. Theorem Proving OntoEngine Concerns Concerns Necessary inferences Necessary 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.

17. 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.”

18. Experiment 2 bbn_ged -> 21164 facts (3010 individuals & 1422 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 facts instead of spouseIn, has husband and wife. bbn_ged infers from marriage and gender. map (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))…)

19. Exp 2 Results 85555 reasoning steps to generate all 26956 facts @ 59 seconds. 85555 reasoning steps to generate all 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")

20. Ontology Extension Generation If we know the relationships of existing ontologies (A & B) and we want to generate subonotolgies of those, what inferences can we assume about the relationships between our created subontologies (c & d). Automatic Updates (propagating changes to other levels of ontologies) in PDDAML Automatic Updates (propagating changes to other levels of ontologies) in PDDAML Example provided using Congo’s delivery system, in which they automatically created a fairly close match to what their experts created manually. Example provided using Congo’s delivery system, in which they automatically created a fairly close match to what their experts created manually.

21. Problems Future Work w/ Ontology Extension When generating ontology extensions, PDDAML can translate the types, predicates and only those axioms that are sub-properties of O s1 to corresponding properties in O s2 and not general axioms.

22. Querying through different ontologies Example: Genealogy drc_ged Find the name of King Henry VI’s that married him on the date given in the previous example. Example: Genealogy drc_ged Find the name of King Henry VI’s that married him on the date given in the previous example. Find those ontologies that can accept partial drc_ged ontology queries that help us answer our question. Find those ontologies that can accept partial drc_ged ontology queries that help us answer 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))))

23. (@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)) (and (@bbn_ged:sex ?q "F") (@bbn_ged:spouseIn ?q @royal92:@F448@) (and (@bbn_ged:sex ?q "F") (@bbn_ged:spouseIn ?q @royal92:@F448@) (@bbn_ged:marriage @royal92:@F448@ ?m)) (@bbn_ged:marriage @royal92:@F448@ ?m)) The bindings this time are {?q/@royal92:@I1218@}, and {?m/@royal92:event3732}. The bindings this time are {?q/@royal92:@I1218@}, and {?m/@royal92:event3732}. (and (@bbn_ged:name @royal92:@I1218@ ?n) (and (@bbn_ged:name @royal92:@I1218@ ?n) (@bbn_ged:date @royal92:event3732 ?d)) The ultimate result is {?n/"Margaret of Anjou"} and {?d/"22 APR 1445"}. The ultimate result is {?n/"Margaret of Anjou"} and {?d/"22 APR 1445"}.

24. Backward Querying Incomplete tool, a lot of research being done in this area. Incomplete tool, a lot of research being done in this area. Not the focus of this paper Not the focus of this paper

25. Related Work This work relies on inference through bridging axioms and layering logic on top of RDF to get free of relying on descriptions. This work relies on inference through bridging axioms and layering logic on top of RDF to get free of relying on descriptions. Prompt & Chimera- name similarity and taxonomic relation matching Prompt & Chimera- name similarity and taxonomic relation matching GLUE- generates only simple mapping rules GLUE- generates only simple mapping rules Incremental Ontology building – more simple rules, but more complicated algorthims. Incremental Ontology building – more simple rules, but more complicated algorthims.

26. Conclusions Ontology translation Ontology translation  Translating datasets  Generating Ontology Extensions  Querying through different Ontologies Ontology translation thought of as Ontology merging. Ontology translation thought of as Ontology merging. If all ontologies, datasets and queries can be expressed in the same terms, semantic translation can be automatic. If all ontologies, datasets and queries can be expressed in the same terms, semantic translation can be automatic.

27. OntoMerge – Ontology translation server. OntoMerge – Ontology translation server. New avenues for automating production of bridging axioms. New avenues for automating production of bridging axioms. Future Development Tools to validate consistency of generated bridging axioms. Tools to validate consistency of generated bridging axioms. Tools to help experts build axioms through dialogues about the form of the desired relation between ontologies in question. Tools to help experts build axioms through dialogues about the form of the desired relation between ontologies in question.