1. Ontology-Based Approaches to Data Integration
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2. Outline Motivation Introduction of ontology The Role of the ontology
Ontology Representation
Use of Mapping
Ontology Engineering
Example
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3. Motivation The so-called information society demands for
complete access to available information, which
is often heterogeneous and distributed.
Problems:
Finding suitable information sources
Each of the information sources have to work together (interoperability problem)*
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4. Motivation Interoperability problem (information sharing):
full accessibility to data
the accessed data can be processed and interpreted by the remote system
Problems might arise due to heterogeneity of the data.
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5. Motivation Heterogeneity of the data:
Structural heterogeneity—Different information systems store their data in different structures.
Semantic heterogeneity—Consider the content of an information item and its intended meaning.
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6. Motivation In order to achieve semantic interoperability in
a heterogeneous information system, the meaning
of the information that is interchanged has to be
understood across the system.
Semantic conflicts occur whenever two contexts
do not use the same interpretation of the
information.
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7. Motivation Three main causes for semantic conflicts:
Confounding conflicts occur when information
items seem to have the same meaning, but differ in reality.
Scaling conflicts occur when difference systems are used to measure a value.
Naming conflicts occur when naming schemes of information differ significantly.
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8. Motivation The use of ontologies for the explication of
implicit and hidden knowledge is a possible
approach to overcome the problem of semantic
heterogeneity.
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9. Introduction to Ontology
The term “ontology” has long been used in many ways and domain. In the computer science world the ontologies are introduced by Gruber as an “explicit specification of a conceptualization” .
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10. Introduction to Ontology
An ontology gives the name and descriptions of the entities of specific domains using predicates that represent relationship between these entities.
Therefore, an ontology might be used for data
integration tasks because of its potential to describe the semantic of information sources and to solve heterogeneity problems.
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11. Application integration involves aspects of the two mentioned above.
On the other hand, the concepts data integration, application integration and application interoperability are similar but we must differentiate them.
Data integration is concerned with unifying data sharing some common semantics but are originated from unrelated sources.*
Application interoperability attempts to standardize the interfaces among stand-alone applications.
Application integration involves aspects of the two mentioned above.
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12. four main criteria Use of ontology Ontology Representation
Use of Mapping
Ontology Engineering
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13. The Role of Ontologies
Initially, ontologies are introduced as an “explicit specification of a conceptualization”.
Therefore, ontologies can be used in an
integration task to make the content explicit.
Additional tasks
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14. Content Explication
In nearly all ontology-based integration approaches ontologies are used for the explicit
description of the information source semantics.
Differences among them are how the ontologies
are employed.
In general, three different directions can be identified: single ontology approaches, multiple ontologies approaches and hybrid approaches.
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15. Single Ontology approaches
global
ontology
Single ontology approaches use one global ontology providing a shared vocabulary for the specification of the semantics. All information sources are related to one global ontology.
single ontology approach
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16. Single Ontology approaches
Advantage: Simple. It can be applied to integration problems where all information sources to be integrated provide nearly the same view on a domain.
Disadvantage:
It calls for the same view on a domain.
It is susceptible for changes in the information sources.
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17. Multiple Ontologies
In multiple ontology approaches, each information source is described by its own ontology.
In principle, the “source ontology” can be a combination of several other ontologies.
local
ontology
local
ontology
local
ontology
multiple ontology approach
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18. Multiple Ontologies Advantage:
No common and minimal ontology commitment about one global ontology is needed.
It can simplify the integration task and supports the change.
Disadvantage:
It is difficult to compare different source ontology because of the lack of a common vocabulary. (mapping)
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19. Hybrid Ontology Approaches
shared
vocabulary
Similar to multiple ontology approaches the semantics of each source is described by its own ontology.
But in order to make the local ontologies comparable, they are built from a global shared vocabulary.
local
ontology
local
ontology
local
ontology
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hybrid ontology approach

20. Hybrid Ontology Approaches
Advantage:
New sources can easily be added without the need of modification.
Supports the acquisition and evolution of ontologies.
The shared vocabulary makes the source ontologies comparable and avoids the disadvantage of multiple ontology approaches.
Disadvantage:
The existing ontologies cannot easily be used, but have to be redeveloped from scratch.
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21. Additional Roles of Ontologies
Query Model
Some integration approaches use the ontology as the global query schema. (SIMS)
method: reformulate the global query into sub-queries for each appropriates source, collects and combines the query results, and return the results.
Advantage: more intuitive for users
Disadvantage: users have to know the structure and the content of the ontology.
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22. Ontology Representation
Description Logics
Frame-Based Systems
Other Approaches
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23. Extensions of Description logics
CLASSIC
GRAIL
LOOM
OIL (Ontology Interface layer )
OWL (Web Ontology Language)
Extensions of Description logics
CARIN
AL-log
DLR
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24. Frame-Based Systems
F-Logic
OKBC
Ontolingua
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25. Other Approaches Formal Concept Analysis Object Language
Annotated Logics
These approaches often also refer to these models as ontologies, from a knowledge engineering point of view, however, these would not always be regarded as ontologies.
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26. Use of Mappings
We use the term mappings to refer to the connection of an ontology to other parts of the application system.
mappings between ontologies and the information they describe
mappings between different ontologies used in a system
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27. Connection to Information Sources
Ontologies often relate to database scheme,but also to single terms used in the database.
Structure Resemblance: Produce a one-to-one
copy of the structure of the database and encode it in a language that makes automated reasoning possible. The integration is then performed on the copy of the model and easily be tracked to the original data.
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28. Connection to Information Sources
Defining of Terms: These definitions do not correspond to the structure of the database, it is only linked to the information by the term that is defined.
Structure Enrichment: It is the most common approach for relating ontologies to information
sources. It combines the two previously mentioned approaches (local model and additional definitions).
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29. Connection to Information Sources
Meta-Annotation: A rather new approach that add semantic information to an information source. This approach is becoming prominent with the need to integrate information present in the World Wide Web where annotation is a natural way of adding semantics.
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30. Inter-Ontology Mapping
Defined Mappings:
a common approach, to provide the possibility to define mappings.
Lexical Relations
an attempt to provide at least intuitive semantics for mappings between concepts in different ontologies.
Top-Level Grounding
in order to avoid a loss of semantics, one has to stay inside the formal representation language when defining mappings between different ontologies.
Semantic Correspondeness
an approach that tries to overcome the ambiguity that arise from an indirect mappings of concepts via a top-level grounding is the attempt to identify well-founded semantic correspondences between concepts from different ontologies.
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31. Ontological Engineering
Development Methodology
Uschold and Gruninger defined four main phases:
1. Identifying a purpose and scope:
2. Building the ontology
Ontology capture
Ontology coding
Integrating existing ontologies
3. Evaluation: Verification and Validations
4. Guidelines for each phase
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32. Ontological Engineering
Supporting tools
OntoEdit
SHOE’s Knowledge Annotator
DWQ
Protege
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33. An Example Data Integration using Ontologies
Hybrid approach
Ontolingua (Frame-Based)
There are three main stages:
building the shared vocabulary
building local ontologies
defining mappings
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34. Analysis of information source
Building the shared vocabulary
Search for terms (or primitives)
Defining the global ontology
Defining mappings
global ontology local ontology
Analysis of information source
Building local ontologies
Defining the local ontology
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ontology construction model

35. Example Truck Truck Milk Milk Truck_with_ refrigeration Truck_without_
Amount (gallon)
Amount (liter)
Milk
Milk
System 2
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System 1

36. First Stage building the shared vocabulary
fist step: analysis of information source
A complete analysis—e.g., what information is stored, how it is stored, the meaning of this
information, etc.
Problems:
a: Property-type mismatch (liter and gallon)
b: Different classification (truck)
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37. First Stage building the shared vocabulary
second step: search for terms (or primitives)
Choose the list of terms or concepts in agreement with the shared vocabulary.
List: milk gallon amount truck
truck_with_refrigeration
truck_without_refrigeration
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38. First Stage building the shared vocabulary
third step: defining the global ontology
Use the terms chosen in the last step to create the global ontology.
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39. ;;;---------------Classes------------- ;;;Milk
(Define-Class Milk (?X) “the set of types of milks”: Def (And (Thing ?X)))
;;;Gallon
(Define-Class Gallon (?X) “the set of gallons”: Def (And (Thing ?X)))
;;;Truck
(Define-Class Truck (?X) “the set of trucks”: Def (And (Thing ?X)))
;;;Truck_With_Refrigeration
(Define-Class Truck_With_Refrigeration (?X) “the set of trucks with refrigeration”: Def (And (Truck ?X)))
;;;Truck_Without_Refrigeration
(Define-Class Truck_Without_Refrigeration (?X) “the set of trucks without refrigeration”: Def (And (Truck ?X)))
;;; Relations
;;;The_Amount
(Define-Relation The_Amount (?Frame ?Value) “the amount expressed in gallons”: Def (And (Gallon ?Frame) (Number ?Value)))
;;;Transporting
(Define-Relation Transporting (?Truck ?Milk ?Gallon) “the amount of milk transported by a truck”: Def (And (Truck ?Truck) (Milk ?Milk) (Gallon ?Gallon)))
The global ontology
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40. Second stage Building local ontologies
first step: analysis of information source
It is similar to the first stage, but this analysis is performed independently, that is, without taking into account the other information source.
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41. ;;;---------------Classes------------- ;;;Milk …… ;;;Liter ;;;Truck
;;;Truck_With_Refrigeration
……..
;;;Truck_Without_Refrigeration
;;; Relations
;;;The_Amount
;;;Transporting
;;; Classes
;;;Milk ……
;;;Gallon
;;;Truck
;;; Relations
;;;The_Amount
;;;Transporting
Ontology 1
Ontology 2
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42. Third stage Defining Mapping
Define the mapping (and relations) between the concepts defined in the global ontology and in the local ontologies.
In our example: A liter equals 0.22 gallon.
(<=>(Liter ?x) (Gallon ?x*(0.22)))
No mapping is needed for the truck classes in both systems because they have the same name and they denote the same thing.
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43. Advantages
This example is based on an hybrid ontology approach, it has two main advantages:
New information sources can be added without need of modification. Only the terms and relations that are not in the global ontology must be added.
The shared vocabulary and the mappings make local ontologies comparable.
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44. Our Work Architecture: hybrid ontology approach
About
Our Work
Architecture: hybrid ontology approach
Ontology Representation: OWL (Description Logics)
Supporting tool: Protégé
Mapping
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45. Thank you!
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