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Multilingual Information Exchange

Multilingual Information Exchange. APAN, Bangkok 27 January 2005 Margherita.sini@fao.org. The general problem. Searching for multilingual resources is not easy: on the web on metadata catalogues / bibliographical databases on full text documents

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Multilingual Information Exchange

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  1. Multilingual Information Exchange APAN, Bangkok 27 January 2005 Margherita.sini@fao.org

  2. The general problem • Searching for multilingual resources is not easy: • on the web • on metadata catalogues / bibliographical databases • on full text documents • Results are generally in the language used in the search query => We need a multilingual approach and multilingual tools (Thesauri / Ontologies, etc.)

  3. What we can achieve (1): Multilingual concept resolution • With a multilingual thesaurus or ontology we can find resources on any language Because we can realize ...... Multilingual concept resolution!

  4. What we can achieve (2): Brokering With a multilingual thesaurus or ontology we can find resources from several sources also if we do not know the terminology and the language used in these sources fishing vessel ships navio navire 船舶 vesselscrafts bateau de pêche fishing boat fishing vessels Results in multiple languages from multiple databases

  5. How to build a multilingual Thesaurus / Ontology • Lexicalizations of concepts in multiple languages: • {… fishing boat; bateau de pêche;捕捞渔船… } • For every language we can have synonyms: • { … fishing vessel, fishing boat, fishing craft … } • { … bateau de pêche, navire de pêche, … } • { … 捕捞渔船, … }

  6. FAO activities (ongoing) • Food safety ontology (English, Spanish, French) • Fishery ontology (English, Chinese) • Food and Nutrition ontology-based portal (English, Spanish, French) • Extensive work with AGROVOC • RDFS / OWL version • Semantic refinements • Expand multilingual coverage • Expand subject coverage

  7. The multilingual vocabulary... • Must cover all concepts of interest to the users in the various languages, • ... at a minimum all domain concepts lexicalized in any of the participating languages • Must accommodate hierarchical structures suggested by different languages (Dr. Soergel)

  8. Problems (1) • Translation of an English thesaurus into German does not make a German thesaurus => whenever possible we need to consider the concept in his globality (many languages, definitions, “surrounding context” etc.) • Equivalence of terms holds only in some contexts • More difficult to translate non-specialized terms (Dr. Soergel)

  9. Problems (2) • Two terms mean almost the same thing but differ slightly in meaning or connotation: • English: alcoholism • French: alcoholisme • English: vegetable (includes potatoes) • German: Gemüse (does not include potatoes) • If the difference is big enough, one needs to introduce two separate concepts under a broader term; otherwise a scope note needs to clearly instruct indexers in all languages how the term is to be used so that the indexing stays, as far as possible, free from cultural bias or reflects multiple biases by assigning several descriptors. (Dr. Soergel)

  10. Available resources: example • SuperThes, ... • SWAD-Europe initiative: thesaurus activities • RDF encoding of multilingual thesaurus • Multilingual labelling approach (mirroring relations for every language) • Interlingual mapping approach (different structures to be mapped)

  11. SWAD-Europe: Inter-Thesaurus Mapping • SKOS mapping: • Exact • Inexact • Major • Minor • Partial • Broad • Narrow • AND • OR • NOT

  12. Inter-Thesaurus Mapping: example <ag:Concept> <descriptor xml:lang="fr">Academie</descriptor> <map:exactMatch> <map:AND> <map:memberList rdf:parseType="Collection"> <aat:Concept> <descriptor xml:lang="en">Academy</descriptor> </aat:Concept> <aat:Concept> <descriptor xml:lang="en">Buildings</descriptor> </aat:Concept> </map:memberList> </map:AND> </map:exactMatch> </ag:Concept>

  13. Available resources: another possibility • Use OWL • Define concepts • Define terms • Define string • Define relationships between these 3 elements: • <similatTo>, <equivalentTo>, (+ skos suggestions) • <hasSynonym>, <hasAntonym>, <hasCognate> • <hasSpellingVariant>, <hasTranslation>

  14. Available resources: other techniques NLP • Knowledge discovery: helps on the creation of ontologies in a specific language • Used to create good IS • Concept extraction • Multilingual search engine • …

  15. Conclusion • We need multilingual tools • Ontologies better than traditional thesauri • The task is not easy • Subject experts are essential • NLP could help • We need tools • To help experts to realize the mapping • To do annotations • …

  16. Live demo http://www.fao.org

  17. Thank you.

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