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I am needing to develop a product taxonomy for foods and beverages that would work across multiple food providers. I need this to provide a code hierarchy for mapping specific products in a categorical fashion, e.g.

Large Latte -IS-A-> Coffee Beverage -IS-A-> Beverage

Double espresso -IS-A-> Coffee Beverage -IS-A-> Beverage

The only thing that I've found so far is a USDA Food Group dictionary that has some high level categories.

So, to be clear, in response to the confusion I seemingly have caused others trying to help, I'm looking for a code-dictionary that provides hierarchical (taxonomic) categories of food products typically found in restaurants.

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  • I'm not sure I follow the "->". Are those categorical inclusions? I assume you've looked at dbpedia.org and wikidata.org?
    – user3856
    Commented Aug 6, 2016 at 23:51
  • Thanks, Barry. The arrows do indeed represent categorical inclusions - ontologically they would be is_a directed edges. The domain is quite tight, in that I am looking only for food products that would be found in typicall restaurants, and where I would label the leaves with closest parent nodes. Ontology is the highest form of result here, whereas even a taxonomical mapping would prove useful. Think of these categories as "food products I can buy in the covered restaurants".
    – Shawn Mehan
    Commented Aug 7, 2016 at 0:00
  • OK, I just edited your question to make it clearer that you had two rows there, instead of one row, and also corrected the spelling in one row. dbpedia.org may help: dbpedia.org/page/Latte and dbpedia.org/page/Espresso though not double espresso. I haven't looked at wikidata for this specific question but it's fairly good too.
    – user3856
    Commented Aug 7, 2016 at 0:33
  • have you looked at the lives spec for health inspections? it may not get this in depth, but sounds kind of similar to what you want.
    – albert
    Commented Aug 8, 2016 at 4:14

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I'm not aware of an open data ready-made taxonomy that's tuned for restaurants, but you can start with nutrient databases (like the Canadian one: http://www.hc-sc.gc.ca/fn-an/nutrition/fiche-nutri-data/index-eng.php) and reverse engineer the 5,690 foods they describe into a taxonomy. If you can consider a commercial dataset, John Snow Labs (www.johnsnowlabs.com) have built a food taxonomy for mobile health apps, so it covers both 'common' meals and restaurant foods.

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