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SEEK Semantic Mediation

SEEK Semantic Mediation. Shawn Bowers Bertram Ludäscher e-Science Centre, May 11-14, 2004,. Outline. The Sparrow Toolkit Semantic Registration Ontology-Driven Structural Transformation. Outline. The Sparrow Toolkit Semantic Registration Ontology-Driven Structural Transformation.

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SEEK Semantic Mediation

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

  2. Outline • The Sparrow Toolkit • Semantic Registration • Ontology-Driven Structural Transformation

  3. Outline • The Sparrow Toolkit • Semantic Registration • Ontology-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 Toolkit • Semantic Registration • Ontology-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 deep maximal ontology/annotation leverage ontology depth shallow course fine annotation granularity

  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? • The name “lt” itself refers to latitude? • The set of values in the column taken as a whole make up a latitude (like coverage) • 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 lat lon Latitude Longitude

  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 lat lon Latitude Longitude

  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 integratewith another dataset withtwo lat/lons? What do we do? Location lat lon Latitude Longitude * 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 verysimple cases,annotationscan become complex… start end Transect Location lat lon Latitude Longitude

  20. Executable, Fine-Grain Semantic Registration genus species count lat lon 'Acanthomyops' 'latipes' 1 41.6, -119.383'Acromyrmex' 'versicolor' 1 33.1839 -114.866'Anergates‘ 'atratulus' 1 37.9833 -84.5167'Anergates‘ 'atratulus' 4 38.8833 -77.1167 Each row represents a RatioMeasurement RatioMeasurement

  21. Executable, Fine-Grain Semantic Registration (cont.) genus species count lat lon 'Acanthomyops' 'latipes' 1 41.6, -119.383'Acromyrmex' 'versicolor' 1 33.1839 -114.866'Anergates‘ 'atratulus' 1 37.9833 -84.5167'Anergates‘ 'atratulus' 4 38.8833 -77.1167 For a row, count is the value of the measurement RatioMeasurement LocalInteger value dataValue 1

  22. Executable, Fine-Grain Semantic Registration (cont.) genus species count lat lon 'Acanthomyops' 'latipes' 141.6 -119.383'Acromyrmex' 'versicolor' 1 33.1839 -114.866'Anergates‘ 'atratulus' 1 37.9833 -84.5167'Anergates‘ 'atratulus' 4 38.8833 -77.1167 For a row, lat/lon are the locations values of the measurement RatioMeasurement LocalInteger value dataValue 1 LocationContext context GeogCoordPoint location latitude 41.6 longitude -119.383

  23. Executable, Fine-Grain Semantic Registration (cont.) genus species count lat lon 'Acanthomyops' 'latipes'141.6 -119.383'Acromyrmex' 'versicolor' 1 33.1839 -114.866'Anergates‘ 'atratulus' 1 37.9833 -84.5167'Anergates‘ 'atratulus' 4 38.8833 -77.1167 For a row, genus/species are mapped to standard values, associated RatioMeasurement … Count itemMeasured TaxonomicGroup propertyEntity SimpleTaxonomicId taxonomicID Genus genus rankName taxon:1883/5 subCat superCat species rankName Species taxon:1883/3

  24. Querying based on Semantic Registrations RatioMeasurement LocalInteger value dataValue 1 LocationContext context GeogCoordPoint location latitude 41.6 longitude -119.383 Count itemMeasured TaxonomicGroup propertyEntity SimpleTaxonomicId taxonomicID Genus genus rankName taxon:1883/5 Find all datasets that measure species of ‘Acanthomyops’ in South Africa … and return a set of all lat/lon “points”(demo …) subCat superCat species rankName Species taxon:1883/3

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

  26. Finding user interfaces that are easy-to-use, but provide detailed annotations <<ontology view>> <<sample instance view>> <<annotation, schema, and data>> resource id: antweb:040412 <<registration information/properties>> Value Value Value TaxaConceptID lat lon count genus species 41.6 -119.4 5 ‘Manica’ ‘bradleyi’ 34.9 -120.7 2 ‘Formica’ ‘fusca’

  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 Toolkit • Semantic Registration • Ontology-Driven Structural Transformation

  29. root population = (sample)* elem sample = (meas, lsp) elem meas = (cnt, acc) elem cnt = xsd:integer elem acc = xsd:double elem lsp = xsd:string Example Structural Types (XML) structType(P2) structType(P3) root cohortTable = (measurement)* elem measuremnt = (phase, obs) elem phase = xsd:string elem obs = xsd:integer <population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> … <population> <cohortTable> <measurement> <phase>Eggs</cnt> <obs>44,000</acc> </measurement> … <cohortTable> P2 P3 P5 P1 S1(life stage property) S2(mortality rate for period) P4

  30. Example Semantic Types Portion of SEEK measurement ontology appliesTo MeasContext 0:* hasContext 1:1 hasProperty itemMeasured MeasProperty Observation Entity 0:* 1:* EcologicalProperty AccuracyQualifier hasLocation Spatial Location AbundanceCount LifeStage Property 1:1 hasValue 1:1 hasCount Numeric Value 1:1

  31. Example Semantic Types Semantic types for P2 and P3 MeasContext Observation hasContext appliesTo LifeStage Property 1:1 1:1 itemMeasured hasCount semType(P3) Abundance Count Number Value 1:1 1:1 1:1 ⊑ hasValue hasProperty semType(P2) AccuracyQualifier 1:1 P2 P3 P5 P1 S1(life stage property) S2(mortality rate for period) P4

  32. The Ontology-Driven Framework Ontologies (OWL) Compatible (⊑) SemanticType Ps SemanticType Pt Registration Mapping (Input) Registration Mapping (Output) StructuralType Ps StructuralType Pt Source Service Target Service Pt Ps Desired Connection

  33. The Ontology-Driven Framework Ontologies (OWL) Compatible (⊑) SemanticType Ps SemanticType Pt Registration Mapping (Input) Registration Mapping (Output) StructuralType Ps StructuralType Pt Correspondence Source Service Target Service Pt Ps Desired Connection

  34. The Ontology-Driven Framework Ontologies (OWL) Compatible (⊑) SemanticType Ps SemanticType Pt Registration Mapping (Input) Registration Mapping (Output) StructuralType Ps StructuralType Pt Correspondence (Ps) Generate Source Service Target Service Transformation Pt Ps Desired Connection

  35. Datasets used in the Prototype genus species count lat lon 'Acromyrmex' 'versicolor‘ 1 33.1839 -114.866… Antweb genus species cnt lt ln Camponotus‘ ‘festinatus‘ 3 30.55 -103.833… South Africa Museum mbcnt cfcnt lat lon 1 2 -25.35 -77.1167… “faked” genus1 species1 genus2 species2 Manica parasitica Manica bradleyi… Dulosis Parasite/Host

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