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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

Generating Application Ontologies from Reference Ontologies

Marianne Shaw

Todd Detwiler

Jim Brinkley

Dan Suciu

University of Washington

motivation
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
Problem Statement
  • How can we enable large ontologies to be used to create application ontologies?
problem statement4
Problem Statement
  • How can we enable large ontologies to be used to create application ontologies?
  • Approach: Add Views to SPARQL
outline
Outline
  • Motivation / Problem Statement
  • SPARQL
  • Our Solution: vSPARQL
    • Subqueries
    • Recursive Queries
    • Skolem Functions
  • Radiology Example
  • Related Work
  • Conclusions
the basics sparql
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 }

outline7
Outline
  • Motivation / Problem Statement
  • SPARQL
  • Our Solution: vSPARQL
    • Subqueries
    • Recursive Queries
    • Skolem Functions
  • Radiology Example
  • Related Work
  • Conclusions
vsparql
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
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

subqueries querying over an existing query10
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

subqueries querying over an existing query11
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

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

recursive queries gathering subgraphs13
Recursive queries: Gathering subgraphs
  • What if we only want a portion of an ontology?
    • Only want parts of the liver

Liver

recursive queries gathering subgraphs14
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

recursive example all parts of the liver
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

skolem functions combining data from two ontologies

Pressure

FluidPressure

OPB

Aortic

Blood

FMA

Skolem Functions: Combining data from two ontologies
  • Ontology of Physics for Biology (OPB)
  • FMA
skolem functions combining data from two ontologies17
Skolem Functions: Combining data from two ontologies
  • OPB
  • FMA
  • Combine OPB:FluidPressure, FMA:AorticBlood

Pressure

FluidPressure

OPB

AorticBlood

Pressure

Aortic

Blood

FMA

skolem functions combining data from two ontologies18
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

skolem functions combining data from two ontologies19
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

outline20
Outline
  • Motivation / Problem Statement
  • SPARQL
  • Our Solution: vSPARQL
    • Subqueries
    • Recursive Queries
    • Skolem Functions
  • Radiology Example
  • Related Work
  • Conclusions
example radiology ontology from fma
Example: Radiology Ontology from FMA
  • All of the visible parts of the liver‏
radiology ontology results
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> ;

radiology ontology results23
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
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.
wrapup
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