Words do not exist in isolation. They might have synonyms and antonyms. They could be part of an ontological set (a beak is a part of a bird and so is a feather, a swallow is a type of bird and so is a goose). They might have spawn (light begat lightsaber), or close relatives (operation and operator). Knowing how words are related enhances our understanding of how a language works, and thus makes it easier to put language to work.

Within the Kamusi architecture, we can pinpoint relationships at a level of granularity that has not previously been available. Because each individual meaning of a word is its own entry, we can know, for example, that heat and warmth could be substituted for each other as synonyms in the context of a chilly person near a fire, without getting confused like Google Translate that someone who is packing heat is putting warmth = chaleur = cordialité in a suitcase. These nuanced associations among🔢 data elements will create opportunities for Natural Language Processing that can underlie next-generation advances in language technology.


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•A = Active language, aligned and searchable
•c = Data 🔢 elicited through the Comparative African Word List
•P = Pending language, data in queue for alignment
•w = Data from 🔠🕸 WordNet teams

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