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  • RELATIONAL DATABASE SCHEMA TO ONTOLOGY MAPPING APPROACHES

    by Yassaman Zand Moghaddam and Joe D. Horton

    November 10th

    2010

  • Outline

    A brief description of mapping and correspondenceCorrespondence PatternsDatabase to ontology mapping process

    A nave solutionRelational database to ontology mapping approachesOntology matchingMatching evaluation parametersThe ideaEvaluation methodAn overview of the MapOnto tool

    Relational Database Schema to Ontology Mapping Approaches

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  • What do Mapping

    and Correspondence

    mean?

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    Mapping is the process of finding relationships or correspondences between entities.Correspondence is the relation holding or supposed to hold according to a particular mapping algorithm between entities.Each correspondence set a bridge between entities.

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  • Correspondence Patterns

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    Correspondence patterns (Scharffe & Fensel, 2008) are like templates to model ontology alignments (The Alignment is the output of ontology mapping). They capture regularities recurring when aligning ontologies.In many applications and domains, specific correspondence patterns exist. Relational database to ontology mapping is one of those applications.

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  • Correspondence patterns identified in relational database to ontology mapping application:

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    Direct MappingJoin/Union MappingProjectionSelectionValue transformationCombinations

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  • Database to Ontology Mapping Process

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    At a conceptual level a database and an ontology are semantically related and correspondences are established between the database components and the ontology components.

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  • Database to Ontology Mapping Approaches

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    Figure1. Classification of database to ontology mapping approaches (Ghawi & Cullot, 2007)

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  • Discovering mappings between a relational database schema and an ontology usually has two phases:

    Relational Database Schema to Ontology Mapping Approaches

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    Searching for simple mappings between entities in the relational database schema and the ontology.

    Constructing complex compositions based on simple mappings.

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  • A nave solution

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    Find the shortest connection between concepts.It involves:

    Finding the minimum spanning tree(s) connecting the corresponded conceptsEncoding the tree(s) into formulas

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  • An example of the nave solution (An, Borgida, & Mylopoulos, 2005)

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    Figure 2. Relational table, Ontology, and Correspondences (An, Borgida, & Mylopoulos, 2006).

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  • An example of the nave solution

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    Simple correspondences:T : Employee.ssn O : Employee.hasSsnT : Employee.name O : Employee.hasNameT : Employee.dept O : Department.hasDeptNumberT : Employee.proj O : Worksite.hasNumber

    Expected formula:T :Employee(ssn,name,dept,proj) :-O:Employee(x1), O:hasSsn(x1,ssn), O:hasName(x1,name), O:Department(x2), O:works_for(x1,x2), O:hasDeptNumber(x2,dept), O:Worksite(x3),

    O:works_on(x1,x3), O:hasNumber(x3,proj).

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  • An example of the nave solution

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    In some cases, because of the known relational schema design rules, the spanning tree may not provide the desired semantics for a table.

    Project (name, manager ) -> manager is functionally dependent on name

    T : Project.name O : Worksite.hasName

    T : Project.manager O : Employee.hasSsn

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    WorksiteEmployee works _on

    1..*

    0..1

  • An example of the nave solution

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    We need to find a functional connection from Worksite

    to Employee

    Project (name, manager )

    WorksiteEmployee

    controls

    1..10..1Department1..1 0..*

    manages

  • Mapping approaches

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    Some of the approaches that address the problem of mapping databases to ontologies are:

    RDB2Onto (Laclavk, 2007)DB2OWL (Cullot, Ghawi, & Ytongnon, 2007)Relational OWL (Prez & Conrad, 2005)VisAVis (Konstantinou, Spanos, Chalas, Solidakis, & Mitrou, 2006)Discovering Simple Mappings Between Relational Database Schema and Ontologies (Hu & Qu, 2007)

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  • Mapping approaches

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    Another Literature review on the topic Database to Ontology Mapping in (Sataya S. et al., 2009), classifies the reviewed approaches based on:

    Mapping CreationMapping Representation and AccessibilityMapping ImplementationQuery ImplementationApplication DomainData Integration

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  • Ontology matching Figure2.

    Classification of ontology matching techniques (Euzenat & Shvaiko, 2007)

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  • Ontology matching Figure2. Classification of ontology matching techniques (Euzenat & Shvaiko, 2007)

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  • Ontology matching Figure3. Extracted from Classification of ontology matching techniques (Euzenat & Shvaiko, 2007)

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  • Matching evaluation parameters

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    Precision =

    Recall =

    F-Measure = 01

    Overall =

    1: True Correspondences2: Derived CorrespondencesA: False Negative

    C: False PositiveB: True Positive

    D: True Negative

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    1 2

    DA B C

  • The idea

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    Inputs:

    Outputs:

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    RDB Schema

    Ontology

    Intermediate Graph

    Intermediate GraphMatching

    (Structure- based)

    A set of correspondences Some

    string- based checks

    A refined set of correspondences

  • Graph isomorphism: An example (source: wikipedia)

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    An isomorphism of graphs G and H is a bijection between the vertex sets of G and H such that any two vertices u and v of G are adjacent in G if and only if f(u) and f(v) are adjacent in H.

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    G:

    H:

    f(a) = 1f(b) = 6f(c) = 8f(d) = 3f(g) = 5f(h) = 2f(i) = 4f(j) = 7

    An isomorphism between G and H:

    f: V(G) V(H)

  • String-based Check

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    String-based techniques are often used in order to match names and name descriptions of entities. These techniques consider strings as sequences of letters in an alphabet. In general, they are based on the intuition: the more similar the strings, the more likely they are to represent the same concepts. Usually, distance functions map a pair of strings to a real number, where a smaller value of the real number indicates a greater similarity between the strings. Examples: prefix, suffix, edit, and n-gram distances.

  • Evaluation Method

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    One method can be the repetition of the process in reverse which means:construction of the relational database schema and the ontology from intermediate graphscomparison of results with original ones (inputs).

    If they are equivalent, there is a high probability that the algorithm can work correctly on any inputs.

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  • MapOnto Tool (An, Borgida, & Mylopoulos, 2006 )

    A system for constructing complex mappings between ontologies and relational or XML database schemas.

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    The inputs of the tool are:An ontology specified in an ontology representation language like OWLA relational or XML database schemaA set of simple correspondences

    The output of the system is:An ordered list of complex mapping formulas expressed in Horn clauses.

    The issue is:Simple mappings between the relational database schema and the ontology are specified manually.

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  • MapOnto Evaluation

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    Datasets that were used in evaluation of the MapOnto tool are in different real world domains and were implemented independently.

    Table1. Characteristics of Schemas and ontologies for the experiments (An, Borgida, & Mylopoulos, 2006).

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  • Relational Database Schema to Ontology Mapping Approaches

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    Thank You

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  • References

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    [1] Yuan An, Alexander Borgida, and John Mylopoulos, Constructing Complex Semantic Mappings Between (XML) Data and Ontologies, International Semantic Web Conference, 2005, pp. 6-20.

    [2] -, Inferring Complex Semantic Mappings Between Relational Tables and Ontologies

    from Simple Correspondences, (OTM) Conferences (2), 2005, pp. 1152-1

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