Eectiveness of Automatic Translations for

Accessing or integrating data lexicalized in dierent languages is a challenge. Multilingual lexical resources play a fundamental role in reducing the language barriers to map concepts lexicalized in dierent languages. In this paper we present a large-scale study on the eectiveness of automatic translations to support two key cross-lingual ontology mapping tasks: the retrieval of candidate matches and the selection of the correct matches for inclusion in the nal alignment. We conduct our experiments using four dierent large gold standards, each one consisting of a pair of mapped wordnets, to cover four dierent families of languages. We categorize concepts based on their lexicalization (type of words, synonym richness, position in a subconcept graph) and analyze their distributions in the gold standards. Leveraging this categorization, we measure several aspects of translation eectiveness, such as word-translation correctness, word sense coverage, synset and synonym coverage. Finally, we thoroughly discuss several ndings of our study, which we believe are helpful for the design of more sophisticated cross-lingual mapping algorithms.