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Ontology Alignment

by

Murat Şensoy

murat.sensoy@boun.edu.tr

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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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Introduction

“Ontology is a formal specification of a conceptualization.” Gruber, 1993

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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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Meaning is in Connections

W i

n

e

i

s

m

a

d

e

f r

o

m

G

r

a

p

e

Wine

is made from

Grape

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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? !!!

?

? ??

?

??

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

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The End

THANK YOU

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The Challenge of Communication