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11/8/20051 Ontology Translation on the Semantic Web D. Dou, D. McDermott, P. Qi Computer Science, Yale University Presented by Z. Chen CIS 607 SII, Week 7
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11/8/20052 Overview Main Content: Ontology Translation Ontology Merging and Automated Reasoning Dataset translation, extension generation and querying Motivation Web agent should understand and process web data Ontology: formalization of web contents (voc, axiom) Ontologies are very different Proposed Solution: Ontology Translation Step 1: Ontology merging: union of terms and axioms Step 2: Bridging axioms are manually added Step 3: Automatic reasoning with theorem prover
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11/8/20053 Why Ontologies Are Different? Syntactically Different DAML+OIL, OWL, WSDL, etc. Semantically Different Different taxonomic structures of concepts. Examples: firstname/lastname and full name Yale’s term for Article, Inproceedings, Incollection, and CMU’s term for Article Semantic difference can be inherited (birth inherited event)
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11/8/20054 Ontology Translation Problems Dataset Exchange Problem To exchange information between ontologies Ontology Extension Generation Given O1, O2 and O1’s extension O1s, construct O2s. Example: DAML-S (app), WSDL (protocol), Congo and/or BravoAir (extend DAML-S), construct ontology in protocol level (extend WSDL)? Query from Multiple Ontologies Knowledge may be in multiple knowledge bases
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11/8/20055 Closely Related Work Difference with Ontology Mapping Automatic discovery of mapping rules (correspondence) Unlikely to be fully automatic due to: Accuracy, Complexity of mapping rules Ontology translation is based on a small set of axioms (question: bridging axiom vs. mapping rules) Ontolingua Any ontology from/to a “generic” ontology Unlikely to scale well OntoMorph Case-by-case translation (dataset-by-dataset) Not general methodology; always case-by-case A Small Summary: Automation in the *right* level
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11/8/20056 Dataset Translation
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11/8/20057 Three Examples of Web-PDDL
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11/8/20058 Semantic Translation Problem: Given a set of facts in one vocabulary, infer the largest possible set of consequences in another. Merge: Union of terms and axioms (automatic? The paper says manual construction by experts). Adding bridging axioms (at best semi-automatic) Relate symbols in one ontology to symbols in another.
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11/8/20059 Merge: Bridging Axiom Figure 2. A Bridging Axiom Figure 3. Term generating functions
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11/8/200510 Inferences Inference Engine Is Used for: Forward chaining to reform facts Backward chaining to reform query Introducing term-generation functions
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11/8/200511 Ontology Extension Generation Problem: O1, O2 and O1s, how about O2s? Similar to Dataset Translation Take the two ontologies as source and target Take the extended ontologies as fact dataset Use inference engine to generate translated facts Create new predicates for the translated facts and make them subproperties of the predicates in the conclusion. Then generate the corresponding axioms for subproperty relationships Evaluations Only translate predicates, types and axioms about subproperties; not currently working for more general axioms.
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11/8/200512 Query through Different Ontologies Problem: Knowledge from different ontologies, or even unable to translate the query by a single ontology Steps: query selection and query reformulation Query selection: chose a simple one and got answered by an (potentially merged) ontology Query reformulation: backward chaining to reform the rest subqueries and get another seleciton Evaluations Query optimization is not done yet
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11/8/200513 Questions Why specifying bridging axioms is easy? Evaluation of inference engine? Completeness problem? What is the best logic?
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11/8/200514 Thank you! Presented by Zebin Chen CIS 607 SII, Week 7
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