@CONFERENCE\{IMM2012-06534,
    author       = "F. K. Clückstad and M. M{\o}rup",
    title        = "Feature-based Ontology Mapping from an Information Receivers’ Viewpoint",
    year         = "2012",
    pages        = "34-43",
    booktitle    = "9th Int. Workshop on Natural Language Processing and Cognitive Science ({NLPCS} 2012): In Conjunction with {ICEIS} 2012",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6534-full.html",
    abstract     = "This paper compares four algorithms for computing feature-based similarities between concepts respectively possessing a distinctive set of features. The eventual purpose of comparing these feature-based similarity algorithms is to identify a candidate term in a Target Language (TL) that can optimally convey the original meaning of a culturally-specific Source Language (SL) concept to a {TL} audience by aligning two culturally-dependent domain-specific ontologies. The results indicate that the Bayesian Model of Generalization [1] performs best, not only for identifying candidate translation terms, but also for computing probabilities that an information receiver successfully infers the meaning of an {SL} concept from a given {TL} translation."
}