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Outline
Introduction to Ontologies
Ontology Alignment
Current Approaches for Ontology Alignment
Using Ontology Alignment in Service Selection
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Ontologies Ontologies are about vocabularies and their
meanings, with explicit, expressive, and well-defined semantics, possibly machine-interpretable.
Main elements of an ontology: Concepts Relationships
Hierarchical Logical
Properties Instances (individuals)
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For machines...
Wine is made from Grape
<Sentence><Subject>
Wine</Subject><Verb>
is made from</Verb><Object>
Grape</Object></Sentence>
XML document
We are defining the structure of document by XML
The meaning of the document is not defined. Machines cannot understand it.
but now the meaning of the structure is not defined.
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<Sentence><Subject>
</Subject><Verb>
</Verb><Object>
</Object></Sentence>
Wine
is made from
Grape
Ontology gives the meaning...
DocumentOntology
Natural Language
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Ontology Alignment Problem
Ontology is used to support interoperability and common understanding between different parties.
Ontologies themselves may have some heterogeneities.
Ontology Alignment is needed to find semantic relationships among entities of ontologies.
How should I
use them? !!!
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dc
b
a
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An Example of Alignment
FastAli’s
Peugeot
VehicleHas
Specification
Speed
250 km/h
Peugeot 405
Has Speed
Car
Speed
Ali
Owner
Boat
Thing
Automobile
Object
Vehicle
Has Owner
1.0
0.6
0.6
0.8
Car – Automobile Label Similarity = 0.0 Super Similarity = 1.0 Instance Similarity = 0.6 Relation Similarity = 0.8 Total Similarity = 0.6
Concept
Property
Instance
Type
Similarity
Car : Ontology A ( ? ) Automobile : Ontology B
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An Example of Ontology Merging
Family Car
Porsche
Sport Car
Automobile
ThingObject
Luxury CarFamily Car
Sport Car
Vehicle
CarBus
BMW
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An Example of Ontology Merging
Object
Luxury CarFamily Car
Sport Car
Family CarSport Car
Automobile
Thing
Vehicle
CarBus
Porsche
BMW
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An Example of Ontology Merging
Sport Car
Automobile
Thing
Family Car
Porsche
Object
Luxury CarFamily Car
Sport Car
Vehicle
CarBus
BMW
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An Example of Ontology Merging
Object, Thing
Luxury Car Family CarSport Car
Vehicle
Car, AutomobileBus
PorscheBMW
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Heterogeneity in Ontologies
Coverage: cover different portions – possibly overlapping– of the world.
Granularity: One ontology provides a more (or less) detailed description of the same entities.
Perspective: an ontology may provide a viewpoint, which is different from the viewpoint adopted in another ontology.
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Overcoming Heterogeneity Using Similarity Terminological Methods
String Based Methods Token Based Methods Language Based Methods
Structural Methods Internal Structure External Structure
Extensional (based on instances) Methods When the classes share the same instances When they do not
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Terminological Methods Terminological methods compare strings.
Can be applied to: name, label comments concerning entities URI
Take advantage of the structure of the string (as a sequence of letter).
The main idea in using such measures is the fact that usually similar entities have similar names and descriptions in different ontologies.
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Terminological M., cont. (String Based)
Substring Similarity Hamming Distance N-Gram Distance Edit Distance Jaro Similarity
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Terminological M., cont (String Methods)
In string edit distance, the operations usually considered are insertion of a character, replacement of a character by another and deletion of a character.
Levenstein Distance is an Edit Distance with all costs to 1.
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Terminological M., cont. (Language Based) Rely on using NLP techniques to find associations
between instances of concepts or classes.
Intrinsic methods: perform the terminological matching with the help of morphological and syntactic analysis to perform term normalization. (Stemming) : going go
Extrinsic methods: make use of external resources such as dictionaries and lexicons (Wordnet).
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Structural Methods The structure of entities that can be found in ontology
can be compared, instead of comparing their names or identifiers.
Internal Structure: use criteria such as the range of their properties (attributes and relations), their cardinality, and the transitivity and/or symmetry of their properties to calculate the similarity between them.
External Structure: The similarity comparison between two entities from two ontologies can be based on the position of entities within their hierarchies.
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Structural Methods (External) If two entities from two ontologies are similar, their
neighbors might also be somehow similar.
Criteria for deciding that the two entities are similar include: Their direct super-entities are already similar. Their sibling-entities are already similar. Their direct sub-entities are already similar. All (or most) of their descendant-entities (entities in the sub
tree rooted at the entity in question) are already similar. All (or most) of their leaf-entities are already similar. All (or most) of entities in the paths from the root to the
entities in question are already similar.
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Extensional (based on instances) Methods Compares the extension of classes, i.e., their set of
instances rather than their interpretation.
These techniques can be used when the classes share the same instances
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Using Learning Methods
Supervised learning can be used for ontology alignment.
Ontology alignment algorithm learns how to work through the presentation of many good alignment (positive examples) and bad alignments (negative examples).
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Example
vehicle
van
hotel
roadvehicle
campervan
vehicle
van
hotel
roadvehicle
campervan
Suppose Ag1 intends to convey van(a)
Ag1 : AddConcept(van)
Ag1 : Provide negative/pozitif examples
Ag2 interprets van as a subclass of roadvehicle and superclass of campervan
Provided Examples
van Not van
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Existing Works
Method Year Organization Project Leader Automatic
Features
Ag
greg
ation
Lexical
Stru
cture
Strin
g
Sem
antic
Instan
ce
OntoMorph 1997 S. California Chalupsky Semi T
U.S. Army 1999 DARPA Semi T
Smart 1999 Sanford Fridman, Noy Semi T T
Chimaera 1999 Stanford McGuinness Semi T T T
Prompt 2001 Stanford Noy, Musen Semi T T
InfoSlueth 2001 Amsterdam Ding Semi T T
A. Prompt 2002 Stanford Noy, Musen Semi T T T
Glue 2002 Illinois Doan Automatic T T T T
IF Map 2003 Southampton Kafoglou Automatic T T
NOM 2003 Karlsruhe Ehric Automatic T T T T T
QOM 2004 Karlsruhe Ehric Automatic T T T T
CROSI 2005 Southampton Kafoglou Automatic T T T
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Service Selection
In the problem of service selection, consumer agents cooperate to identify service providers that would satisfy their service
needs the most.
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Service Selection Consumers communicate about their service needs.
Different consumers may have different service needs
Some consumers may come up with new service needs
Ontologies evolve separately depending on the needs of the
consumers
Use Credit Card
Need
buy Use Internet
Buy over Internet using credit card
Buy using credit card
Need
buy Use Internet
Buy over Internet
Use Credit Card
Consumer Agent 1 Consumer Agent 2
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Service Selection Consumer1 requests information related to “Buying over internet using credit card”
Consumer2 does not understand the request
Consumer2 should learn what Consumer1 means.
How can consumer2 add the concept to its own ontology?
Using the terminological methods: syntax of the concept etc.
Using structural methods: properties of the concept
Using the instances related to the concept
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Service Selection Terminological methods may not be used, concepts with similar meaning may be labeled highly different. Especially in our case, agents creates new concepts and name of these concepts may be irrelevant to semantics.
Structural methods are good candidates, because services are already defined in terms of their properties and these properties can be used to map different service concepts or service needs.
Instance-based methods are good candidates. However, in this context, what is an instance?
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Service Selection
In current approaches, ontologies evolve separately. This results in distinct ontologies.
It may be a good idea to evolve ontologies cooperatively. This results in overlapping ontologies.
Advantages: Ontologies are aligned over time
Useful concepts emerge rapidly