Use of FCA in the Ontology Extraction Step for the Improvement of the Semantic Information Retrieval Peter Butka (Peter.Butka@tuke.sk), TU Ko - PowerPoint PPT Presentation

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Use of FCA in the Ontology Extraction Step for the Improvement of the Semantic Information Retrieval Peter Butka (Peter.Butka@tuke.sk), TU Ko

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Semantic Web Environment and Retrieval Tasks ... negoti 0.127356, nehru 0.14293, kashmir 0.149645, india 0.169807, pakistan 0.200657 ... – PowerPoint PPT presentation

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Title: Use of FCA in the Ontology Extraction Step for the Improvement of the Semantic Information Retrieval Peter Butka (Peter.Butka@tuke.sk), TU Ko


1
Use of FCA in the Ontology Extraction Step for
the Improvement of the Semantic Information
RetrievalPeter Butka (Peter.Butka_at_tuke.sk), TU
Košice, Slovakia
  • Semantic Web Environment and Retrieval Tasks
  • Information retrieval improvement of unknown set
    of text documents
  • Preprocessing of documents set
  • Building of the ontology
  • Creating of concept hierarchy
  • Finding of relations between concepts
  • Extraction of instances (ontology population)
  • Using of created ontology and instances for the
    improvement of IR

2
Use of FCA and labeling of concepts
  • Formal Concept Analysis
  • Explorative method for data analysis
  • Concept lattice
  • Concept is cluster of similar objects
    (similarity is based on presence of same
    attributes)
  • Concepts are hierarchically organized (specific
    vs. general)
  • Use of FCA on texts
  • Output one-sided fuzzy concept lattice
  • Clustering via concepts (agglomerative)
  • Interpretation
  • Use of LabelSOM method for improving of
    interpretability of concepts (clusters)

Concept (467) IntSet 467(467 2) 6
59 Labels (467) indian 0.0849, provinc
0.125887, negoti 0.127356, nehru 0.14293, kashmir
0.149645, india 0.169807, pakistan 0.200657
3
Possible use of reduction approach to ontology
creation step
  • FCA and texts
  • interpretability problems
  • time-consuming
  • Solution problem reduction gt e.g. use of
    clustering algorithms
  • Pre-clustering of document set (e.g. Hierarchical
    SOM)
  • Creation of ontology parts from smaller sets
    (FCA)
  • Merging of small models to complete ontology
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