Article by: Farshad Hakimpour, Andreas Geppert

Article Summary by Mark Vickers

Presentation Layout

Introduction Overview Phase I - Merging Ontologies Phase II - Generating Global Ontology Phase III - Data Mapping Conclusion Assessment

Introduction

Goal : Global Schema Generation for Information System Integration

• Focus on semantics of words (NOT names of schema elements or schema structure)

• Use of Formal Ontologies

• Assumes: Formal Ontologies for local schemas already available

Overview

Phase I

•Merge Formal Ontologies

•Using Similarity Relations

Phase II

•Build Global Schema

•Based on Merged Ontology

Phase III

•Data Mapping

•From Global to Local

Merging Ontologies

Phase I

Phase II

Phase III

Match the intentional definitions from different Ontologies

How do you know the meaning of the schema elements?

Answer: local formal ontologies

Where do the ontologies come from?

Answer: Community Consensus

Who links the schema names to ontology terms?

Answer: Database Designer

How is the matching done?

Answer: Using Similarity Relations

Similarity Relations

Phase I

Phase II

Phase III

Equality, specialization, overlapping and disjoint relations between intentional definitions in two different formal ontologies

With two terms pTi and qTj defined in formal ontologies p and q, with tau mapping a term to its intentional definition:

pTi is Equal to (or synonym of) qTj if and only if both intentional definitions are the same:

Similarity Relations

Phase I

Phase II

Phase III

pTi is a Specialization (or hyponym) of qTj if and only if the conjunction of the two definitions is the same as the definition of pTi (then qTj is a Generalization or hypernym of pTi):

Similarity Relations

Phase I

Phase II

Phase III

pTi is Overlapping with qTj if and only if the conjunction of the two definitions is not false for all possible states of the world:

Tk is called conjunction concept or conjunction relation

Intentional Definition

Phase I

Phase II

Phase III

High level ontology for both ontologies p and q

Part of ontology p

Part of ontology q

“Salary” is a specialization of “Wage”

Global Schema Generation Phase II

Phase I

Phase III

Integrating two schemas, Sp1 and Sq1, from different ontologies p and q

Two parts:• Class Integration• Attribute Integration

Class Integration Names of schemas must be based on concept definitions in

the community's formal ontology Example:

class “Resident” in schema Sq1 is based on the term “Person” defined in a formal ontology p

(tau links a schema class to an ontology term)

Phase II

Phase I

Phase III

Class Integration

Global Class Derivation:• For every class in local schema, create a class in global

schema

• If and equal concept is already present, store alias in existing class

• Specializations in merged ontology are subclass relation in global schema

• New classes based on conjunctions may be added

• Need for supervision due to relevancy of application

• Super concept classes are added if referred to by two overlapping or disjoint classes

Phase II

Phase I

Phase III

Attribute Integration All attributes in the schema represent binary

relations

Phase II

Phase I

Phase III

For each attribute in a local class’s schema, define an attribute in the respective class in the global schema

Same rules apply for equal, specialization relations EXCEPT we keep the relation link between them for data mapping

• Example:

Global Schema Generation Phase II

Phase I

Phase III

Global Schema Generation Phase II

Phase I

Phase III

Mapping of instances of classes in local DB to global schema and vice versa

Straight forward Relies on info kept during the integration

process

Data Mapping Phase III

Phase I

Phase II

Two problems:1. Mapping a superclass to its subclassSolution: classification criterion

2. Two instances are classified under one class in the global schema, while they represent the same individual in the domain

Solution: identification criterion

Data Mapping Phase III

Phase I

Phase II

Two quality measures for success Community accordance on ontology Details of explicit specifications of

implicit assumptions in the community while building ontologies

Conclusion

Well thought out, clean approach Limited scope

• 1:1 mapping only

• Assumes agreed upon high level ontologies

• DBs designed to link to local ontologies) Accordance on high level ontologies,

though difficult seems to be inevitable as we face this difficult problem

Assessment