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Phaneendra Varma Maryala

Semantic Conflict Resolution Ontology SCORL : An Ontology for Dtecting and Resolving Data and Schema-Level Semantic Conflicts. Sudha Ram and Jinsoo Park. Phaneendra Varma Maryala. Outline:. Introduction Need for Semantic Ineroperability SCORL Methods for Detection Semantic Conflicts

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Phaneendra Varma Maryala

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  1. Semantic Conflict Resolution OntologySCORL: An Ontology for Dtecting and Resolving Data and Schema-Level Semantic Conflicts.Sudha Ram and Jinsoo Park Phaneendra Varma Maryala

  2. Outline: Introduction Need for Semantic Ineroperability SCORL Methods for Detection Semantic Conflicts Examples Evaluation and Conclusion

  3. Introduction: • Ontology: why? Have been used to capture domain knowledge for knowledge based systems. So what? • Establishing a Semantic Interoperability among heterogeneous info So How? • Common ontology : Semantic Conflict Resolution Ontology

  4. Semantic Interoperability: Resolving Various Context dependent Incompatibilities i.e semantic conflicts. Ex: tax assesments Tackled by 2 approaches: Federated Schema & b)Domain ontology SCORL : automatic ways of detecting and dynamic resolving semantic conflicts in heterogeneous DB’s. Consists of two distinct sets : Concepts and Instances. -Facilitates sharing and reuse -Mappings can be associated with each DB.

  5. Development: Issues to be considered: Contents : Semantic conflict Types to be resolved Construction and Maintenance: Challenging issue (no prior information) Mapping: Information source to common Ontology Relevance

  6. SCROL construct: Structure is a Tree. SCORL is a tuple ∧= ( OC, OI, RS, RM, u) OC: Concept: Name , Definition, Subconcept, Subconcept-of, Referenced-by OI: Instances: Name, Def, Instance-of, Referenced-by (< s, o, t, l>) RS: Sibling Relationship : (disjoint, peer, is-a, part-of) RM: Mapping Relationship U is the root of ∧. (no parent)

  7. Graphical notation of SCORL Constructs: • Relationships: (Symmetric) • Disjoint – not semantically equivalent • Peer – One-one mapping (transformed into each other) Note: Many Kinds of instances. • Asymmetric: • Is-a: Generalization (EX: Water) • Part-of : Aggregation(EX: City)

  8. Structure Of SCORL: Tree

  9. Peer Relationship:one to one semantics example:

  10. Semantic Conflict Detecting and Resolution: CREAM( conflict resolution Environment for Autonamous Mediation) ACREAM frameworkis an 8-tuple∆= (Γ,Σ,Ξ,Λ,Ω,Φ,Ψ,Θ) Semantic Conflicts at 2 levels: DATA & SCHEMA level each having 6 kinds Diff in Data Domains Diff in Logical Structures

  11. Data Level Conflicts: Data value conflict ( Ex: soil) Data representation conflict ( Ex: date) Data Unit Conflicts (Ex : centimeters & inches) Data Precision conflicts ( Ex: grades) Spatial domain conflict Known data value reliability conflicts

  12. Schema Level Conflicts: Naming Conflict ( Ex: entity classes, relationships) Entity identifier conflicts ( Ex: primary keys) Schema isomorphism conflicts Generalization ( Ex: Surface water & Ground Water) Aggregation Conflicts Schematic discrepancies

  13. Data level conflicts encoded within SCROL

  14. Defining Ontology Relationship and Mapping Knowledge ORK : relation on σ×σ×λ (σ € (OC Ù OI) in Λ ) Example for ORK

  15. ORK= {(Square Meter, Acre, ),(City,County,part-of)} Establishing a Mapping Knowledge between SCORL and DB schemas. OMK: set of mappings between SCORL × (FS × LS) And is OMK is a subset of it. Considering previous figure Ex: OMK = {(Area,REGION.area),(Acre,CITY_SIZE.gross_size),(Square Meter,COUNTY_Area.area),(Region,REGION)(City,CITY_SIZE),(County,COUNTY_AREA) }.

  16. We discussed that SCORL is used to detect and resolve data and schema conflicts. Data Conflict:

  17. Explanation: LAND_PARCEL.temperature in local schema 1 and LAND_PARCEL.avg_temperature in local schema 2 are semantically equivalent, and both are mapped to LAND_PARCEL.avg_temperature in the feder-ated schema Find the other ?????? In SCORL Area and Temperature are conflict controllers which have disjoint relationship ( semantically equivalent ) yes/no = ? After detection – resolving it Target context (LAND_PARCEL.avg_temp)

  18. Schema Level Conflict:Sum of collected tax amounts (in thousands) of different taxes each year for a particular country.

  19. Find the conflict? Conflict Resolver determines the tax type in the local schema 1 by looking at each data value stored in the REAL_PROPERTY.tax_type and then assigns each data value captured in the REAL_PROPER-TY.tax_amount to the corresponding attribute in the federated scema. Observe diff in local schema 3.

  20. Evaluation: All of the conflicts found at the data level and schema level were resolved by the semantic mediators using SCROL and ontology mapping knowledge

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