SEEK Semantic Mediation Shawn Bowers Bertram Ludäscher e-Science Centre, May 11-14, 2004,

Outline The Sparrow ToolkitThe Sparrow Toolkit Semantic RegistrationSemantic Registration Ontology-Driven Structural TransformationOntology-Driven Structural Transformation

Outline The Sparrow ToolkitThe Sparrow Toolkit Semantic RegistrationSemantic Registration Ontology-Driven Structural TransformationOntology-Driven Structural Transformation

Semantic Mediation in SEEK: Our focus Resource Discovery –Ontology-driven tools to help search for datasets and services using semantic descriptions … Data Transformation –Determine and execute mappings to compose services and bind data to services Data Integration –Provide reconciled, uniform access to multiple datasets “Semantic” Workflow Analysis –Verify semantic correctness, accumulate semantic information, and provide workflow planning/suggestion services … the future

The Sparrow Toolkit: Vision Lightweight Languages and command-line-style services to support mediation –Syntax and language conversion DL, FOL, OWL, RDF, … –Reasoning subsumption, classification, consistency, satisifiability, datatypes, instance classification, … –Display utilities hierarchies, OO/ER style models, OWL DLs? –Query Query answering, semantic query rewriting, semantic registration, integration, … Logic-based implementation (Prolog)

Some sparrow-dl (Taxon example)

Some more sparrow-dl (“textbook” example)

display_formulas(KB)

display_preclassified_hierarchy(K)

display_classified_hierarchy(K)

display_classified(K)

Outline The Sparrow ToolkitThe Sparrow Toolkit Semantic RegistrationSemantic Registration Ontology-Driven Structural TransformationOntology-Driven Structural Transformation

Adding semantics to EML: Observations The finer grain the annotation, the more opportunity for discovery, integration, and transformation … The coarser grain the annotation, the harder it is to do useful operations; unless your ontology is very deep annotation granularity ontology depth finecourse shallow deep maximal ontology/annotation leverage

Semantic Registration (SSDBM’04) By annotation granularity, we mean: –Resource-Level “Metadata” –Attribute Level (the attribute itself) –Attribute Level (as a collection-value) –Attribute Level (as independent values) –Attribute Groups (as a collection-value or independent values) –Filtered values (e.g., SQL where-clause) –Specific value annotations (as a mapping function or stated by- hand) Often, integration and transformation require very detailed annotations

Some Examples (arguments against concepts-as-labels) r(…, lt, ln, …) sem(lt) == latitude sem(ln) == longitude Question: What do these annotations mean? 1.The name “lt” itself refers to latitude? 2.The set of values in the column taken as a whole make up a latitude (like coverage) 3.Each individual value in the column denotes a separate latitude (Is it a latitude though? Or just a coded rep.?) We want to avoid these ambiguous anntotations … often

Some Examples (still not enough) r(…, lt, ln, …) sem(lt) == values represent latitude sem(ln) == values represent longitude More problems: How do I know lt and ln go together to form a location, for example, … Location LatitudeLongitude latlon

Some Examples (still not enough) r(…, lt, ln, lt-end, ln-end, …) sem(lt) == values represent latitude sem(ln) == values represent longitude sem(lt-end) == values represent latitude sem(ln-end) == values represent longitude Which lat goes with which lon? Location LatitudeLongitude latlon

Some Examples (still not enough) r(…, lt, ln, lt-end, ln-end, …) sem(lt, ln) == values represent location and lat leads to semval(lt) and lon leads to semval(ln) ** sem(lt, ln) == values represent location sem(lt) == values represent latitude sem(ln) == values represent longitude sem(lt, ln) == values represent location and … sem(lt-end) == values represent latitude sem(ln-end) == values represent longitude What if we want to integrate with another dataset with two lat/lons? What do we do? Location LatitudeLongitude latlon * We could infer the lat and lon roles here; in general, I don’t think we can infer roles as such…

Some Examples (still not enough) r(…, lt, ln, lt-end, ln-end, …) sem(lt, ln, lt-end, ln-end) === values represent transect and start leads to semval(lt, ln) and end leads to semval(lt-end, ln-end) sem(lt, ln) == values represent location and … sem(lt) == values represent latitude sem(ln) == values represent longitude sem(lt, ln) == values represent location and … sem(lt-end) == values represent latitude sem(ln-end) == values represent longitude So, even in very simple cases, annotations can become complex… Location LatitudeLongitude latlon Transect start end

Executable, Fine-Grain Semantic Registration genusspeciescountlatlon 'Acanthomyops''latipes' , 'Acromyrmex''versicolor' 'Anergates‘'atratulus' 'Anergates‘'atratulus' Each row represents a RatioMeasurement RatioMeasurement

Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 'Acanthomyops''latipes' , 'Acromyrmex''versicolor' 'Anergates‘'atratulus' 'Anergates‘'atratulus' For a row, count is the value of the measurement value 1 dataValue RatioMeasurement LocalInteger

Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 'Acanthomyops''latipes' 'Acromyrmex''versicolor' 'Anergates‘'atratulus' 'Anergates‘'atratulus' For a row, lat/lon are the locations values of the measurement value 1 dataValue context location latitude longitude RatioMeasurement LocalInteger LocationContext GeogCoordPoint

Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 'Acanthomyops''latipes' 'Acanthomyops''latipes' 'Acromyrmex''versicolor' 'Anergates‘'atratulus' 'Anergates‘'atratulus' For a row, genus/species are mapped to standard values, associated RatioMeasurement itemMeasured Count propertyEntity TaxonomicGroup … taxonomicID SimpleTaxonomicId genus Genus rankName taxon:1883/5 Species species rankName taxon:1883/3 subCat superCat

Querying based on Semantic Registrations value 1 dataValue context location latitude longitude RatioMeasurement LocalInteger LocationContext GeogCoordPoint itemMeasured Count propertyEntity TaxonomicGroup taxonomicID SimpleTaxonomicId genus Genus rankName taxon:1883/5 Species species rankName taxon:1883/3 subCat superCat Find all datasets that measure species of ‘Acanthomyops’ in South Africa … and return a set of all lat/lon “points” (demo …)

Architecture SMS Operations Dataset repository (heterogeneous) Lat/Lon Species Queries Semantic Annotations Taxon Services Synonyms Concept IDs … Ontology repository Results discover_resources query_resources integrate_resources Mappings

Finding user interfaces that are easy-to-use, but provide detailed annotations genusspecieslatloncount TaxaConceptIDValue ‘Manica’‘bradleyi’ ‘Formica’‘fusca’ resource id: > antweb:040412

A Sparrow Executable Semantic Annotation Registration A partial object instantiation (of onto classes) The resource can be queried directly using the object structure (i.e., using the ontology)

Outline The Sparrow ToolkitThe Sparrow Toolkit Semantic RegistrationSemantic Registration Ontology-Driven Structural TransformationOntology-Driven Structural Transformation

Example Structural Types (XML) S 1 (life stage property) S 2 (mortality rate for period) S 2 (mortality rate for period) P1P1 P2P2 P4P4 P3P3 P5P5 root population = (sample)* elem sample= (meas, lsp) elem meas= (cnt, acc) elem cnt= xsd:integer elem acc= xsd:double elem lsp= xsd:string 44, Eggs … root cohortTable= (measurement)* elem measuremnt= (phase, obs) elem phase= xsd:string elem obs= xsd:integer Eggs 44,000 … structType(P 2 ) structType(P 3 )

Example Semantic Types Portion of SEEK measurement ontology MeasContext ObservationEntityMeasProperty hasContext 0:* 1:1 appliesTo hasProperty 0:* Accuracy Qualifier Ecological Property Abundance Count LifeStage Property Numeric Value Spatial Location hasLocation hasCount 1:1 hasValue 1:1 itemMeasured 1:*

Example Semantic Types Semantic types for P2 and P3 S 1 (life stage property) S 2 (mortality rate for period) S 2 (mortality rate for period) P1P1 P2P2 P4P4 P3P3 P5P5 Observation semType(P 3 ) MeasContext hasContext 1:1 appliesTo LifeStage Property 1:1 Abundance Count itemMeasured Number Value hasCount 1:1 semType(P 2 ) ⊑ Accuracy Qualifier hasProperty 1:1 hasValue 1:1

The Ontology-Driven Framework Source Service Source Service Target Service Target Service PsPs PtPt Semantic Type P s Semantic Type P t Structural Type P t Structural Type P s Desired Connection Compatible (⊑)(⊑) Registration Mapping (Output) Registration Mapping (Input) Ontologies (OWL)

The Ontology-Driven Framework Source Service Source Service Target Service Target Service PsPs PtPt Semantic Type P s Semantic Type P t Structural Type P t Structural Type P s Desired Connection Compatible (⊑)(⊑) Registration Mapping (Output) Registration Mapping (Input) Correspondence Ontologies (OWL)

The Ontology-Driven Framework Source Service Source Service Target Service Target Service PsPs PtPt Semantic Type P s Semantic Type P t Structural Type P t Structural Type P s Desired Connection Compatible (⊑)(⊑) Registration Mapping (Output) Registration Mapping (Input) Correspondence Generate (Ps)(Ps) (Ps)(Ps) Ontologies (OWL) Transformation

Datasets used in the Prototype genusspeciescountlatlon 'Acromyrmex''versicolor‘ … genusspeciescntltln Camponotus‘‘festinatus‘ … Antweb South Africa Museum mbcntcfcntlatlon … “faked” genus1species1genus2species2 ManicaparasiticaManicabradleyi … Dulosis Parasite/ Host