Maria, who works with multiple European and Asiatic languages, suggested this years ago.
If all languages had one-to-one correspondences of nouns, verbs, adverbs, prepositions and adjectives (just differently spelled or drawn and differently pronounced) and used the same syntax – the structure that gives a string of words meaning – it would be a relatively trivial exercise to realize a long-standing dream, accurate machine translation of text from one language to another, with preservation of the information content and ‘meaning’ of the translation.
Unfortunately, that’s not the way languages have evolved. There are very often not simple one-to-one correspondences between words in different languages, and there are significant structural differences among them. Even in cases where there appear to be simple correspondences, the context of terms apparent to the author and the reader may be difficult to manage for machine translation. For example, the word “Florence” could refer to Florence Nightingale or to an Italian city (with a different Italian spelling in the latter case).
Google Translator represents years of experience in machine translation from one language to another, including a lot of empirical ‘tuning’ based on vocabularies and contexts. The actual results of translations, in terms of preservation of intended meaning, range from pretty good to awful. The files corresponding to each language are large, and processing takes time, whether by direct access to those files or via Internet access to them.
So DEVONthink doesn’t provide an automatic means of searching across a database comprised of documents in different languages, to pull related content regardless of the language in which it was written.
But the syntax of searches in DEVONthink 2 allows mixing of terms, exact strings and wildcards in highly structured queries of indefinite length.
That means that a user who has a database containing multiple languages, and who is familiar with the correspondences of terms among those languages, can create a query to search across them. This requires user familiarity with the terms used in the various languages, and perhaps long nested disjunctions in the query. If you create such queries, you will probably want to save them as text documents in order to avoid reconstruction each time such a search is needed.
Which brings me to an interesting point. I once talked about how it’s possible to ‘teach’ the See Also artificial intelligence feature via ‘bridges’ between terms that may not exist already in the documents contained in a database. The example I used was the canine family, which includes wolves, dogs, foxes and coyotes. If I were to create a new document that contains a multiply-repeated phrase such as “canine/canines: wolf/wolves, dog/dogs, fox/foxes, coyote/coyotes” and then view a document that’s about foxes but which doesn’t contain the term ‘canine’, See Also may then see relationships to other documents about canines, wolves, etc.
In other words, the act of saving those laboriously constructed queries that search across multiple languages could build bridges between documents in different languages.