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

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

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

4. 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

5. 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)

6. Some sparrow-dl (Taxon example)

7. Some more sparrow-dl (“textbook” example)

8. display_formulas(KB)

9. display_preclassified_hierarchy(K)

10. display_classified_hierarchy(K)

11. display_classified(K)

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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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…

19. 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

20. Executable, Fine-Grain Semantic Registration genusspeciescountlatlon 'Acanthomyops''latipes' 1 41.6, -119.383 'Acromyrmex''versicolor' 133.1839-114.866 'Anergates‘'atratulus' 137.9833-84.5167 'Anergates‘'atratulus' 438.8833-77.1167 Each row represents a RatioMeasurement RatioMeasurement

21. Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 'Acanthomyops''latipes' 1 41.6, -119.383 'Acromyrmex''versicolor' 133.1839-114.866 'Anergates‘'atratulus' 137.9833-84.5167 'Anergates‘'atratulus' 438.8833-77.1167 For a row, count is the value of the measurement value 1 dataValue RatioMeasurement LocalInteger

22. Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 141.6 -119.383 'Acanthomyops''latipes' 1 41.6 -119.383 'Acromyrmex''versicolor' 133.1839-114.866 'Anergates‘'atratulus' 137.9833-84.5167 'Anergates‘'atratulus' 438.8833-77.1167 For a row, lat/lon are the locations values of the measurement value 1 dataValue context location latitude longitude 41.6 -119.383 RatioMeasurement LocalInteger LocationContext GeogCoordPoint

23. Executable, Fine-Grain Semantic Registration (cont.) genusspeciescountlatlon 'Acanthomyops''latipes'141.6 -119.383 'Acanthomyops''latipes' 1 41.6 -119.383 'Acromyrmex''versicolor' 133.1839-114.866 'Anergates‘'atratulus' 137.9833-84.5167 'Anergates‘'atratulus' 438.8833-77.1167 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

24. Querying based on Semantic Registrations value 1 dataValue context location latitude longitude 41.6 -119.383 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 …)

25. 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

26. Finding user interfaces that are easy-to-use, but provide detailed annotations genusspecieslatloncount TaxaConceptIDValue 41.6-119.45‘Manica’‘bradleyi’ 34.9-120.72‘Formica’‘fusca’ resource id: > antweb:040412

27. 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)

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

29. 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 44,000 0.95 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 )

30. 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:*

31. 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

32. 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)

33. 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)

34. 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

36. Datasets used in the Prototype genusspeciescountlatlon 'Acromyrmex''versicolor‘133.1839-114.866 … genusspeciescntltln Camponotus‘‘festinatus‘330.55-103.833 … Antweb South Africa Museum mbcntcfcntlatlon 12-25.35-77.1167 … “faked” genus1species1genus2species2 ManicaparasiticaManicabradleyi … Dulosis Parasite/ Host