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Ontology Translation for the Semantic Web by Dejing Don, Drew Mc. Dermott, and Peishen Ontology Translation for the Semantic Web by Dejing Don, Drew Mc. Dermott, and Peishen Qi

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

Assumptions Ignoring syntatic differences n A merged ontology is formed by: n – the Assumptions Ignoring syntatic differences n A merged ontology is formed by: n – 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)

Software Involved n Onto. Engine – Involved with all three of tasks in the Software Involved n Onto. Engine – Involved with all three of tasks in the problem set n Onto. Merge – Semi-automated (machine assisted expert tool) – Online nexus n PDDAML – Web-PDDL

Test Set One n Yale vs. CMU Yale (yale_bib ontology) Article Inproceedings CMU(cmu_bib onotology) Test Set One n Yale vs. CMU Yale (yale_bib ontology) Article Inproceedings CMU(cmu_bib onotology) Article Incollection

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

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

Translation vs. Mapping As defined by this paper: Mapping: finding or mapping the relationships 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).

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

Translation Choices Global all-encompassing One True Theory ontology that covers all existing ontologies. All 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-thefly ontology translation directly from a dataset in a (source) ontology to a dataset in another (target) ontology, on a dataset-bydataset basis. (Onto. Morph)

Approach n 3 Parts: i. Syntatic translation parse ii. Semantic translation iii. Syntatic translation Approach n 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

Web-PDDL n n Internal representation used by Onto. Engine to make semantic inferences. Extends Web-PDDL n n Internal representation used by Onto. Engine to make semantic inferences. Extends PDDL with XML namespaces and additional axiom notation. (define (domain yale_bib-ont) (: extends (uri "http: //www. w 3. org/2000/01/rdfschema#": prefix rdfs)) (: types Publication – Obj Article Book Incollection Inproceedings - Publication Literal - @rdfs: Literal) (: predicates (author p - Publication a - Literal). . . ))

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

n Sentence : “The publication Breton. Zucker 96 appeared in the Proceedings of IEEE n Sentence : “The publication Breton. Zucker 96 appeared in the Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition" n In yale_bib: (: objects. . . Breton. Zucker 96 - In. Proceedings) (: facts. . . (booktitle Breton. Zucker 96 "Proceedings of CVPR'96")) In cmu_bib: (: objects. . . Breton. Zucker 96 - Article proc 38 - Proceedings) (facts. . . (in. Proceedings Breton. Zucker 96 proc 38) (booktitle proc 38 "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: in. Proceedings a p) (@cmu_bib: booktitle p tl))))) n n

Existentialism in Code (forall (a - @yale_bib: Inproceedings tl - String) (iff (booktitle a Existentialism in Code (forall (a - @yale_bib: Inproceedings tl - String) (iff (booktitle a tl) (exists (p - Proceedings) (and (contain p a) (@cmu_bib: in. Proceedings a p) (@cmu_bib: booktitle p tl))))) p - existential modifier n Skolem term? : n 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.

Theorem Proving Onto. Engine n n Concerns Necessary inferences – Forward chaining from source Theorem Proving Onto. Engine n n Concerns 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.

Onto. Engine Features ü Stops: (protection against theorem loops) • Hard coded limit to Onto. Engine Features ü Stops: (protection against theorem loops) • Hard coded limit to complexity of terms that Onto. Engine can generate • Deductive Engines stops when it reaches conclusions or goals, in the case of backward chaining, in the target ontology. ü Good type-checking system making use of the strong typed feature of Web-PDDL ü Translation Completeness: ü traded completeness for efficiency “Anything that can be expressed in the source ontology can be expressed in the target ontology. ”

Experiment 2 bbn_ged -> 21164 facts (3010 individuals & 1422 families) (@bbn_ged: name @royal Experiment 2 bbn_ged -> 21164 facts (3010 individuals & 1422 families) (@bbn_ged: name @royal 92: @I 1248@ "Francis_II") (@bbn_ged: sex @royal 92: @I 1248@ "M") (@bbn_ged: spouse. In @royal 92: @I 1248@ @royal 92: @F 456@) (@bbn_ged: marriage @royal 92: @F 456 @royal 92: event 3138) (@bbn_ged: date @royal 92: event 3138 "24 APR 1558") (@bbn_ged: place @royal 92: event 3138 "Paris, France") drc_ged -> 26596 facts instead of spouse. In, has husband 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: spouse. In h f) (@bbn_ged: marriage f m)) (husband f h))…)

Exp 2 Results n 85555 reasoning steps to generate all 26956 facts @ 59 Exp 2 Results n 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 @royal 92: @I 1248@ "Francis_II") (@bbn_ged: sex @royal 92: @I 1248@ "M") (@bbn_ged: spouse. In @royal 92: @I 1248@ @royal 92: @F 456@) (@bbn_ged: marriage @royal 92: @F 456 @royal 92: event 3138) (@bbn_ged: date @royal 92: event 3138 "24 APR 1558") (@bbn_ged: place @royal 92: event 3138 "Paris, France") (@drc_ged: name @royal 92: @I 1248@ "Francis_II") (@drc_ged: sex @royal 92: @I 1248@ "M") (@drc_ged: husband @royal 92: @F 456 @royal 92: @I 1248@) (@drc_ged: marriage @royal 92: @F 456 @royal 92: event 3138) (@drc_ged: date @royal 92: event 3138 "24 APR 1558") (@drc_ged: location @royal 92: event 3138 "Paris, France")

Ontology Extension Generation n n If we know the relationships of existing ontologies (A Ontology Extension Generation n n 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 Example provided using Congo’s delivery system, in which they automatically created a fairly close match to what their experts created manually.

Problems Future Work w/ Ontology Extension When generating ontology extensions, PDDAML can translate the 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 Os 1 to corresponding properties in Os 2 and not general axioms.

Querying through different ontologies n n Example: Genealogy drc_ged Find the name of King Querying through different ontologies n n 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. (: 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))))

n n n (@drc ged: name ? k n n n (@drc ged: name ? k "Henry VI") (@bbn_ged: name ? k "Henry VI") {? k/@royal 92: @I 1217@} (@drc_ged: husband? f @royal 92: @I 1217@) {? f/ @royal 92: @F 448@} (and (@drc_ged: wife royal 92: @F 448@ ? q) (@drc_ged: marriage@royal 92: @F 448@? m)) (and (@bbn_ged: sex ? q "F") (@bbn_ged: spouse. In ? q @royal 92: @F 448@) (@bbn_ged: marriage @royal 92: @F 448@ ? m)) The bindings this time are {? q/@royal 92: @I 1218@}, and {? m/@royal 92: event 3732}. (and (@bbn_ged: name @royal 92: @I 1218@ ? n) (@bbn_ged: date @royal 92: event 3732 ? d)) The ultimate result is {? n/"Margaret of Anjou"} and {? d/"22 APR 1445"}.

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

Related Work n n This work relies on inference through bridging axioms and layering Related Work n n 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 GLUE- generates only simple mapping rules Incremental Ontology building – more simple rules, but more complicated algorthims.

Conclusions n Ontology translation Translating datasets Ø Generating Ontology Extensions Ø Querying through different Conclusions n Ontology translation Translating datasets Ø Generating Ontology Extensions Ø Querying through different Ontologies Ø n n 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.

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