semantic integration

1. Semantic integration of data in
database systems and
ontologies
Ing. Petra Šeflová
Technical university of Liberec
Faculty of mechatronics

2. 2
Integration of data
- merging a set given schemas into global
schema
Semantic integration
- part of concept integration of data
- be focusing on data exchange between
applications in the light of their meaning, content
and required business rules

3. 3
Example
Integration of data
homeseekers.com
Source schema
Source schema
Source schema
wrapper
wrapper
wrapper
mediated schema
Find houses
with four
bathrooms
and price
under
$500.000
realestate.com
greathomes.com
A data integration system in the real estate domain.

4. 4
Applications
 Catalog integration in B2B applications
 E-commerce
 Bioinformatics
 P2P Databases
 Agent communications
 Web services Integration

5. 5
Key commonalities application of
Semantic integration
 Use structured representation
(e.g. relational schemas and XML DTDs)
 Must resolve heterogenities with respect to the
schema and their data
 Enable their manipulation
 Merging the schemas
 Computing differences
 Enable translation of data and queries across the
schemas/ontologies

6. 6
• Database schema
– Present definition physical system layout (database)
• Ontology
– System of knowledge about world
• Claimless on coherence (lot of partial ontology)
• Frequently specific created artefact
– Definition of Gruber: Ontology is formal, explicit
specification sharing conceptualization.

7. 7
Problems of Semantic integration
 Semantic of elements can be inferred from only
a few information sources
 Creators of data
 Dokumentation
 Associated schema and data
 Schema element are typically matched based on
clues in the schema and data
 Schema and data clues are often incomlpete
 Matching is often subjective, depending in the
application

8. 8
Matching process
• Take as input two schemas/ontologies,
each consisting of a set discrete entities,
and determine as output the relationships
holding between these entities

9. 9
Location Price ($) Agent-id
Atlanta, GA 360,000 32
Raleigh, NC 430,000 15
Id Name city state
32 Mike Brown Athlanta GA
15 Jean Laup Relaign NC
Area list-
price
Agent-
address
Agent-
name
Denver,
CO
550,000 Boulder,CO Laura
Smith
Atlanta,
GA
370,800 Athens,
GA
Mike
Brown
Schema S
Houses
Schema T
Agents
Example : The schema of two relational database S and T on house
listing, and the semantic correspondence between them

10. 10
Matching techniques
Two groups
 Rule-based
 Learning-based

11. 11
Rule-based solutions
 Many of the early as well as current matching
solutions employ hand-crafted rules
 Exploit schema information
 Element names
 Data types
 Structures
 Integrity constraints
 Can provide a quick and concise method to
capture valuable user knowledge about domain

12. 12
Rule-based solutions
 Benefits
 „relatively inexpensive“
 Do not require training
 Operate only on schema
 Drawback
 They cannot exploit data instance effectively
 They cannot exploit previous matching efforts
For example :
 TranScm
 DIKE
 MOMIS
 CUPID

13. 13
• TranScm
– Employs rules such as
„two elements match if they have the same name
(allowing synonyms) and the same number of
subelements
• DIKE
– Computes similarity between two schema element
based on similarity of the characteristics of the
element and similarity of related elements
• MOMIS
– Compute similarity of schema elements as a
weighted suma of the similarity of name,data type
and substructure
• CUPID
– Employs rules that categorize elements based on
names, data types and domains

14. 14
Learning-based solutions
 Exploit both schema and data information
 They do exploit previous matching efforts
 Examples:
 SemInt system
 LSD system
 iMAP system
 Autocomplex
 Automatch

15. 15
• SemInt
– Uses a neuralnetwork learning approaches
– It matched schema elements based on attribute
specifications and statistic of data content
• LSD
– Employs Naive Bayes over data instance
– Develop novel learning solution exploit the
hierarchical nature of XML data
• iMAP
– Matches the schemas of two sources by analyzing
the description of objects that are found in both
sources
• Autoplex and Automatch
– Use a Naive Bayes learning approach that exploits
data instances to match element

16. 16
The Matching dimensions
• Input dimension
• Process dimension
• Output dimensions

17. 17
Input dimension
• Concern the kind of input on which algorithm
operate
• First dimension
– Algorithms depending on the data/ conceptual model
in which ontologies or schemas are expressed
• Second dimension
– Depend on the kind of data algorithms exploit
– Different approaches exploit different information of
the input data/conceptual models
• Schema-level information
• Instance data
• Exploit both

18. 18
Process dimensions
• Classification of the matching process could be
based on its general properties
• It depends on the approximate or exact nature
of its computation
– Exact algorithms compute the absolute solution to a
problem
– Approximate algorithms sacrifice exactness to
performance
• Three large classes based on intrinsic input,
external resources or some semantic theory
– Syntactic
– External
– Semantic

19. 19
Output dimensions
• Concern the form of the result they produce
– One-to-one correspondence
– Is any relation suitable
– Has it to be final mapping element
• System deliver a graded answer
• Correspondences hold with 98% confidence
• Correspondences hold with 4/5 probability
• All-or-nothing answer
– Correspondences using distance measuring
– Kind of relations between entities a system can
provide
• Equivalence
• Subsumption
• Incompatibility

20. 20
Classification of elementary schema-based
matching approaches
Schema-Based Matching Techniques
Element-level Structure-level
Syntantic
Syntactic External
Linguistic Internal Relational
Semantic
Structural
Terminological
Schema-Based Matching Techniques
Semantic
External
String-
Based
Language-
Based
Linguistic
Resource
Contraint-
Based
Upper
Level
Formal
ontologies
Graph-
Based
Taxonomy-
Based
Repository
of
Structure
Model-
Based
Alignment
reuse
Basic
Techniques
layer
Granuality/Input Interpretation layer

21. 21
Element-level vs structure-level
 Element-level matching techniques
compute mapping elements by analyzing
entities in isolation
 Ignoring their relation with other entities
 Structure-level techniques compute
mapping elements by analyzing how
entities appear together in a structure

22. 22
Internal vs external techniques
 Interal
 Exploiting information which comes only with input
schema/ontologies
 Syntactic interpretation of input
 Sematic interpretation of input
 External
 Exploit auxiliary (external) resources of domain to
interpret the input
 Resources :
 Human input
 Some thesaurus expressing the relationship between terms

23. 23
Schema Matching vs Ontology Matching
Differences
 Database schema often do not provide explicit
semantics for their data
 Semantics is usually specified explicitly at design-
time
 Usually performed with the help of techniques trying
to guess the meaning encoded in the schemas
 Ontologies are logical systems that themselves
obey some formal semantics
 Primarily try to exploit knowledge explicitly encoded
in the ontologies

24. 24
Schema Matchin vs Ontology Matching
Commonalities
 Ontologies and schemas are similar in the
sense :
 Provide a vocablurary of terms that describes a
domain of interest
 Constrain the meaning of terms used in
vocablurary
 Schema and ontologies are found in such
enviroment as the Semantic web

25. 25
Sources :
• Natalya F.Noy : Semantic Integration: A survey of Ontology-Based
Approaches
• AnHai Doan, Alon Y. Haley: Semantic Integration in the Database
Community: A Brief Survey
• P.Schvaiko, J. Euzenat: A Survey of schema-based Matching
Approaches
• G. Antonious, F. van Harmelen: A Semantic Web Primer
• R. Araújo, H. Sofia Pinto: Toward Semantics-based ontology
similarity
• H. Wache, T. Vögele, U. Visser, H. Stuckenschmidt, G. Shuster, H.
Neumann and S. Húbner: Ontology-based integration of information
– A survey existing Approaches
• E. Rahm, P.A. Bernstein: A survey of approaches to automatic
schema matching