Monday, February 11, 2013

Comparison of Semantic Similarity for Different Languages Using the Google n-gram Corpus and Second-Order Co-occurrence Measures. Colette Joubarne, Diana Inkpen. Advances in AI 2011

  • Claims
    • many languages without sufficient corpora to achieve valid measures of semantic similarity. 
    • manually-assigned similarity scores from one language can be transferred to another language, 
    • automatic word similarity measure based on second-order co-occurrences in the Google n-gram corpus, for English, German, and French

Semantic similarity estimation from multiple ontologies. Montserrat Batet, David Sánchez, Aida Valls, Karina Gibert. Appl Intell 2013

  • Claims
    • enable similarity estimation across multiple ontologies
    • solve missing values, when partial knowledge is available
    • capture the strongest semantic evidence that results in the most accurate similarity assessment, when dealing with overlapping knowledge
  • Key ideas
    • Consider  sub-cases
      • both concepts appear in one ontology
      • concepts appear in different ontologies
      • missing concepts
      • etc.
    • requires a taxonomy structure (other relations not useful?)
  • Related work
    • mapping the local terms of distinct ontologies into an existent single one 
    • creating a new ontology by integrating existing ones
    • compute the similarity between terms as a function of some ontological features
    • ontologies are connected by a new imaginary root node
    • matching concept labels of different ontologies
    • graph-based ontology alignment ... by means of path-based
      similarity measures.
    • combines path length and common specificity.
  • Experiments
    • general purpose and biomedical benchmarks of word pairs
    • baseline: related works in multi-ontology similarity assessment.

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