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 p T i and q T j defined in formal ontologies p and q, with tau mapping a term to its intentional definition: p T i is Equal to (or synonym of) q T j if and only if both intentional definitions are the same:

Similarity Relations  Phase I Phase II Phase III p T i is a Specialization (or hyponym) of q T j if and only if the conjunction of the two definitions is the same as the definition of p T i (then q T j is a Generalization or hypernym of p T i ):

Similarity Relations  Phase I Phase II Phase III p T i is Overlapping with q T j if and only if the conjunction of the two definitions is not false for all possible states of the world: T k 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, S p1 and S q1, 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 Sq 1 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 subclass Solution: 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