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FCA-M ERGE: Bottom-up Merging of Ontologies

FCA-M ERGE: Bottom-up Merging of Ontologies. Gred Stumme Alexander Maedche Presenter: Yihong Ding. O1. 1st step. 2nd step. 3rd step. FCA-Merge: method. O1. Merging Algorithm. models. Ontology. Ontology. Ontology Environment . references. uses. Text Processing Server. Domain

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FCA-M ERGE: Bottom-up Merging of Ontologies

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

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

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

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

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

  8. Three Assumptions • Documents have to be relevant. • Documents have to coverall 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. Merging Algorithm models Ontology Ontology OntoEdit models Framework uses Propose new concepts/ relations references uses Text Processing Server Domain lexicon 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 • 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. The blue concept is a subconceptof 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. Merging Algorithm Ontology Environment Framework uses Propose new concepts/ relations models references uses 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 identifiedin 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

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