Tea Blending / OE Spring 2016

There are many kinds of tea in the world. Even when restricting the word to mean 'real tea', made with the leaves of Camellia sinensis, different kinds of processing give rise to green tea, black tea, white tea, oolong tea, pu-erh tea, and so on. Each of these categories has its own categories, from grassy, sharp sencha green tea to smoky gunpowder green tea. Beyond this, there are many tea blends available – Earl Grey, made from black tea and bergamont, or masala chai, black tea mixed with different spices.

A devoted tea drinker might want to make their own blends, and while this could be done by pure experimentation, because tea can be expensive or limited in quantity, and a serious tea drinker might have many options, a suggestion engine would be helpful. Beyond merely listing out combinations, the system should have some intelligence in making recommendations based on the attributes of the teas: style, sub-style, and flavor notes. Delicate teas brewed at low temperatures should not be mixed with teas that require boiling water; some flavor combinations work better than others – a bitter tea may work well with a grassy or vegetal tea, but perhaps not with a metallic one.

With objective brewing temperature restrictions and a crowdsourced rating and suggestion framework both tied to a basic hierarchy of teas and tisanes, we provide the semantic structure upon which to build an application that would allow even newcomers to the world of tea to provide relevant feedback on blends they enjoy, as well as to gain recommendations for new blends to try.

Main ontology
Individuals rdf file
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Tea Blending - Ontology Engineering Spring 2016