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 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

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

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

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

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: CONSTRUCT { fma:Liver ?y ?z } FROM WHERE { fma:Liver ?y ?z }

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

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

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

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 Query1 Alice’s Organ Ontology FMA Query2 Bob’s Liver Ontology FROM NAMED [ CONSTRUCT { … } WHERE { … } ]

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

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

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

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 [ CONSTRUCT { … } WHERE { … } UNION CONSTRUCT { … } FROM WHERE { GRAPH { … } } ] Base case Recursive case

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

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

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

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

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

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

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

Radiology Ontology Results ; ; ;... ; ; ;

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

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 Alkhateeb F, Baget JF, Euzenat J. RDF with Regular Expressions. Views Noy NF, Musen MA. Specifying Ontology Views by Traversal. ISWC Magkanaraki A, et al. Viewing the Semantic Web through RVL lenses. ISWC Miklos Z, et al. Querying Semantic Web Resources using TRIPLE Views. ISWC 2003.

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

Questions?