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Generating Application Ontologies from Reference Ontologies

Generating Application Ontologies from Reference Ontologies. Marianne Shaw Todd Detwiler Jim Brinkley Dan Suciu University of Washington. Motivation. Growing # of specialized ontologies Open Biomedical Ontologies (OBO), >50 ontologies Unified Medical Language System (UMLS), >90 ontologies

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Generating Application Ontologies from Reference Ontologies

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  1. Generating Application Ontologies from Reference Ontologies Marianne Shaw Todd Detwiler Jim Brinkley Dan Suciu University of Washington

  2. Motivation Growing # of specialized ontologies Open Biomedical Ontologies (OBO), >50 ontologies Unified Medical Language System (UMLS), >90 ontologies Link ontologies via reference ontologies Reference ontologies are: Large e.g. Foundational Model of Anatomy (FMA) 75,000 classes; 120,000 terms; 168 relationship types; >2.1 million relationship instances Complex Comprehensive

  3. Problem Statement • How can we enable large ontologies to be used to create application ontologies?

  4. Problem Statement • How can we enable large ontologies to be used to create application ontologies? • Approach: Add Views to SPARQL

  5. Outline • Motivation / Problem Statement • SPARQL • Our Solution: vSPARQL • Subqueries • Recursive Queries • Skolem Functions • Radiology Example • Related Work • Conclusions

  6. The Basics: SPARQL • SPARQL: W3C’s standard for querying RDF • RDF: (subject, predicate, object) triples • Simple SPARQL query over FMA • Creates a new RDF graph • “Get direct properties of liver” PREFIX fma: <http://.../fma_1_4_0#> CONSTRUCT { fma:Liver ?y ?z }FROM <http://.../fma_1_4_0>WHERE { fma:Liver ?y ?z }

  7. Outline • Motivation / Problem Statement • SPARQL • Our Solution: vSPARQL • Subqueries • Recursive Queries • Skolem Functions • Radiology Example • Related Work • Conclusions

  8. vSPARQL • Extend SPARQL to support views • Extensions enable three types of functionality • Querying over existing queries • Gathering subgraphs of an ontology • Creating new data by combining data from multiple ontologies

  9. Subqueries: Querying over an existing query • Alice’s ontology contains info queried from FMA • “Get organs & their direct properties” Query1 Alice’s Organ Ontology CONSTRUCT { ?sub ?prop ?val }FROM <http://.../fma_1_4_0>WHERE { ?sub rdfs:subClassOf fma:Organ . ?sub ?prop ?val} FMA

  10. Subqueries: Querying over an existing query • Alice’s ontology contains info queried from FMA • Bob only interested in Alice’s info about liver • Subqueries allow us to query existing queries Query2 Bob’s Liver Ontology Query1 Alice’s Organ Ontology FROM NAMED <subquery> [ CONSTRUCT { … } WHERE { … }] FMA

  11. Subqueries: Querying over an existing query • Bob only interested in Alice’s info about liver Query2 Bob’s Liver Ontology CONSTRUCT { fma:Liver ?lprop ?lval }FROM <alice_ontology> [ CONSTRUCT { ?sub ?prop ?val } FROM <http://.../fma_1_4_0> WHERE { ?sub rdfs:subClassOf fma:Organ . ?sub ?prop ?val . }]WHERE { fma:Liver ?lprop ?lval .} Query1 Alice’s Organ Ontology FMA

  12. Recursive queries: Gathering subgraphs • What if we only want a portion of an ontology? … … … …

  13. Recursive queries: Gathering subgraphs • What if we only want a portion of an ontology? • Only want parts of the liver … Liver … … … … …

  14. Recursive queries: Gathering subgraphs • What if we only want a portion of an ontology? • Only want parts of the liver Recursive queries allow us to query for arbitrary subgraphs … Liver … … FROM NAMED <recursive_liver> [ CONSTRUCT { … } WHERE { … } UNION CONSTRUCT { … } FROM <recursive_liver> WHERE { GRAPH <recursive_liver> { … } }] Base case … … Recursive case …

  15. Recursive example: All parts of the liver CONSTRUCT { ?sub ?prop ?obj . }FROM NAMED <recursive_liver> [ CONSTRUCT { fma:Liver fma:part ?obj . } FROM <http://.../fma_1_4_0> WHERE { fma:Liver fma:part ?obj . } UNION CONSTRUCT {?c fma:part ?d} FROM <http://.../fma_1_4_0> FROM NAMED <recursive_liver> WHERE { GRAPH <recursive_liver> { ?a ?b ?c . } . ?c fma:part ?d . }]WHERE { GRAPH <recursive_liver> { ?sub ?prop ?obj } } // Base: Direct parts of liver … // Recursive: Transitively, parts of liver Liver … … … … …

  16. Pressure FluidPressure OPB Aortic Blood FMA Skolem Functions: Combining data from two ontologies • Ontology of Physics for Biology (OPB) • FMA

  17. Skolem Functions: Combining data from two ontologies • OPB • FMA • Combine OPB:FluidPressure, FMA:AorticBlood Pressure FluidPressure OPB AorticBlood Pressure Aortic Blood FMA

  18. Skolem Functions: Combining data from two ontologies • OPB • FMA • Combine OPB:FluidPressure, FMA:AorticBlood Skolem Functions generate new nodes from queried info Pressure FluidPressure OPB AorticBlood Pressure Aortic Blood [[ <skolem_function_url>(arg1, ...) ]] FMA

  19. Skolem Functions: Combining data from two ontologies PREFIX fma: <http://.../fma_1_4_0#> PREFIX opb: <http://.../opb#> PREFIX new: <http://.../new_ontology#>CONSTRUCT { [[new:fma_phys(fma:Aortic_Blood,opb:FluidPressure)]] ?p_prop ?p_obj .}FROM NAMED <http://.../fma_1_4_0>FROM NAMED <http://.../opb>WHERE { GRAPH <http://.../fma_1_4_0> { fma:Aortic_Blood ?ab_prop ?ab_obj .} GRAPH <http://.../opb> { opb:FluidPressure ?p_prop ?p_obj .}} Pressure FluidPressure Aortic Blood Pressure OPB Aortic Blood FMA

  20. Outline • Motivation / Problem Statement • SPARQL • Our Solution: vSPARQL • Subqueries • Recursive Queries • Skolem Functions • Radiology Example • Related Work • Conclusions

  21. Example: Radiology Ontology from FMA • All of the visible parts of the liver‏

  22. Radiology Ontology Results <http://.../fma_1_4_0#Right_portal_vein> <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Anterior_branch_of_right_portal_vein> ; <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Caudate_lobe_branch_of_right_portal_vein> ; <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Trunk_of_right_portal_vein> ; <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Posterior_branch_of_right_portal_vein> .<http://.../fma_1_4_0#Segment_of_liver> <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Subsegment_of_liver> .<http://.../fma_1_4_0#Parenchymatous_organ> <http://www.w3.org/2000/01/rdf-schema#subClassOf> <http://.../fma_1_4_0#Organ> .<http://.../fma_1_4_0#Caudate_lobe_of_liver> <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Papillary_process_of_caudate_lobe_of_liver> ; <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Left_segment_of_caudate_lobe_of_liver> ; <http://.../fma_1_4_0#part> <http://.../fma_1_4_0#Caudate_process_of_caudate_lobe_of_liver> ;

  23. Radiology Ontology Results • FMA ontology size • 1.7 million RDF triples • 178MB text file • Radiology Ontology size • 164 RDF triples • 38KB text file

  24. Related Work Subqueries • Schenk S. A SPARQL Semantics Based on Datalog. KI 2007: Advances in Artificial Intelligence. Regular Expressions • Detwiler LT, Suciu D, Brinkley J. Regular paths in SPARQL: Querying the NCI Thesaurus. AMIA’08. • Kochut K, Janik M. SPARQLer: Extended SPARQL for Semantic Association Discovery. ESWC 2007. • Alkhateeb F, Baget JF, Euzenat J. RDF with Regular Expressions. http://hal.inria.fr/inria-00144922/en. Views • Noy NF, Musen MA. Specifying Ontology Views by Traversal. ISWC 2004. • Magkanaraki A, et al. Viewing the Semantic Web through RVL lenses. ISWC 2003. • Miklos Z, et al. Querying Semantic Web Resources using TRIPLE Views. ISWC 2003.

  25. WrapUp • Reference ontologies can be used to link together specialized ontologies • Views can make large reference ontology datasets manageable • vSPARQL extends SPARQL • Subqueries • Recursive queries • Skolem Functions • vSPARQL can be used to generate application ontologies using views over reference ontologies

  26. Questions?

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