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.