FCA-MERGE: Bottom-up Merging of

Ontologies

Gred Stumme Alexander Maedche

Presenter: Yihong Ding

FCA-Merge: method

1st step 2nd step 3rd step

O1

O1

The Framework

OntologyEnvironment models

models

Merging Algorithm

uses

Proposenew concepts/ relations

Domain lexicon

Text Processing Serveruses

references

Lexical DB

OntologyOntology

dictionaries/natural language texts

FCA-Merge

i) Instance extraction (linguistic analysis based) and context generation

ii) FCA-Merge core algorithm that generates the pruned concept lattice

iii) Generating the new ontology from the concept lattice

Framework

Ontology Environment models

models

Merging Algorithms

uses

Proposenew concepts/ relations

Domain lexicon

Text Processing Serveruses

references

Lexical DB

OntologyOntology

dictionaries/natural language texts

Information Extraction Engine (SMES)

Linguistic Knowledge Pool

Lexical database:700.000 word formsNamed entity lexica,compound & taggingrules

Finite State Grammers

Text Chart

Shallow Text Processing

Word Level Sentence Level

Conceptual System

Ontology:Domain-specific semantic knowledge

Domain Lexicon:Domain-specific mappingof words to the Conceptual system

• Tokenizer

• Lexical Processor• POS-Tagger

• Named Entity Finder • Phrase Recognizer• Clause Recognizer

( )

( ) ( )

( )

( )

( )

( ) ( )

Linguistic Analysis and Context Generation

root

furnishing

accomodation

event area

...

hotel youth hostel...

cityregion ...

wellness hotel

Three Assumptions

Documents have to be relevant.

Documents have to cover all concepts.

Documents have to separate the concepts well enough.

FCA-Merge

i) Instance extraction (linguistic analysis based) and context generation

ii) FCA-Merge core algorithm that generates the pruned concept lattice

iii) Generating the new ontology from the concept lattice

Framework

OntoEdit

Ontology

models

models

Merging Algorithm

usesProposenew concepts/ relations

Domain lexicon

Text Processing Serveruses

references

Lexical DB

Ontology

Formal Concept Analysis Arose in the 1980s in Darmstadt as a mathematical

theory

Formalize the concept of concept

Used for deriving conceptual hierarchies from data tables

Provide a visualization of the hierarchies by line diagrams

Used here as a method for conceptual clustering

A formal context about National Parks in California

Intent B

National Parks in California

Ext

en

t A

Def.: A formal concept

is a pair (A,B) where

• A is a set of objects (the extent of the concept),

• B is a set of attributes(the intent of the concept),

• AB is a maximal rectangle in the binary relation.

National Parks in California

The blue concept is

a subconcept of the

yellow one, since its

extent is contained

in the yellow one.

Generating the Pruned Concept Lattice

The ontology concepts are clustered by the algorithm TITANIC.

FCA-Merge

i) Instance extraction (linguistic analysis based) and context generation

ii) FCA-Merge core algorithm that generates the pruned concept lattice

iii) Generating the new ontology from the concept lattice

Framework

OntologyEnvironment

models

Merging Algorithm

Domain lexicon

Text Processing Serveruses

references

Lexical DB

Proposenew concepts/ relations

uses

Generating the new Ontology from the Concept Lattice

Concepts generating the same formal concept are suggested to be merged.

Formal concepts without attributes give rise to new concepts or relations (or subsumptions).

Concepts from the same ontology may also be merged.

Concepts which generate alone a formal concept are taken over into the new ontology.

Ontology Environment OntoMat

FCA-Merge (Summary)

Appearance of concepts in documents is discovered. The concepts are

clustered.

Concepts generating the same cluster are suggested to be merged.

System Summary FCA-Merge approach is extensional, i.e., it is

based on objects which appear in both ontologies. Concepts having the same extent are supposed to

be merged. The idea of FCA-Merge is to create, based on the

source ontologies, a concept hierarchy - the concept lattice -containing the original concepts.

Ontology concepts having the same extent are identified in the concept lattice.

The knowledge engineer can then create the target ontology interactively, based on the insights gained from the concept lattice.

Assessment

Smart, clean, beautiful, learning-based approach Instance-level matching Can only handle 1:1 mappings

But it is possible to extend to 1:n and n:m Works for taxonomic relations

Not sure for non-taxonomic relations Require well-covered, well-separated, and relevant

document sets Derive merged ontology manually, heavily relying on

domain experts’ background knowledge