1. FCA-MERGE: Bottom-up Merging of Ontologies
Gred Stumme Alexander Maedche
Presenter: Yihong Ding

2. FCA-Merge: method O1
1st step
2nd step
3rd step O1

3. The Framework Merging Algorithm Ontology Ontology
uses
dictionaries/natural
language texts
Propose
new
concepts/
relations
Ontology
Ontology
models
Domain
lexicon
Text Processing Server
uses
references
Lexical DB
Ontology
Environment
models

4. FCA-Merge
Instance extraction (linguistic analysis based) and context generation
FCA-Merge core algorithm that generates the pruned concept lattice
Generating the new ontology from the concept lattice

5. Framework Merging Algorithms Ontology Ontology Text Processing Server
uses
dictionaries/natural
language texts
Propose
new
concepts/
relations
Ontology
Ontology
models
Domain
lexicon
Text Processing Server
uses
references
Lexical DB
Ontology
Environment
models

6. Information Extraction Engine (SMES)
Conceptual System
Ontology:
Domain-specific
semantic knowledge
Domain Lexicon:
Domain-specific mapping
of words to the
Conceptual system
Linguistic
Knowledge Pool
Lexical database:
word forms
Named entity lexica,
compound & tagging
rules
Finite State Grammers
Text Chart
( )
( )
( )
( )
( )
( )
( )
( )
Shallow Text Processing
Word Level Sentence Level
Tokenizer
Lexical Processor
POS-Tagger
Named Entity Finder
Phrase Recognizer
Clause Recognizer

7. Linguistic Analysis and Context Generation
root
furnishing
accomodation
event
area
...
hotel
youth hostel
city
region
wellness hotel

8. Three Assumptions Documents have to be relevant.
Documents have to cover all concepts.
Documents have to separate the concepts well enough.

9. FCA-Merge
Instance extraction (linguistic analysis based) and context generation
FCA-Merge core algorithm that generates the pruned concept lattice
Generating the new ontology from the concept lattice

10. Framework Merging Algorithm Ontology Ontology Text Processing Server
uses
Propose
new
concepts/
relations
Ontology
Ontology
models
references
uses
OntoEdit
Text Processing Server
Domain
lexicon
models
Lexical DB

11. 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

12. A formal context about National Parks in California

13. Intent B Extent A National Parks in California 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),
AB is a
maximal rectangle
in the binary relation.
National Parks
in California
Extent A

14. National Parks in California
The blue concept is a subconcept of the yellow one, since its extent is contained in the yellow one.
National Parks
in California

15. Generating the Pruned Concept Lattice
The ontology concepts are clustered by the algorithm TITANIC.

16. FCA-Merge
Instance extraction (linguistic analysis based) and context generation
FCA-Merge core algorithm that generates the pruned concept lattice
Generating the new ontology from the concept lattice

17. Framework Merging Algorithm Text Processing Server Ontology Domain
uses
Propose
new
concepts/
relations
models
references
uses
Ontology
Environment
Text Processing Server
Domain
lexicon
Lexical DB

18. Generating the new Ontology from the Concept Lattice
Concepts from the same ontology may also be merged.
Concepts which generate alone a formal concept are taken over into the new ontology.
Formal concepts without attributes give rise to new concepts or relations
(or subsumptions).
Concepts generating the same formal concept are suggested to be merged.

19. Ontology Environment OntoMat

20. FCA-Merge (Summary)
Concepts generating the same cluster are suggested to be merged.
Appearance of concepts in documents is discovered.
The concepts are clustered.

21. 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.

22. 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