Helping A.I. to Learn About Indigenous Cultures
One such tool is IVOW’s Indigenous Knowledge Graph, or IKG, a cultural engine in early development that is focused on storytelling about Indigenous recipes and culinary practices. After meeting the IVOW team in 2018, Mr. Yarlott pitched the IKG, a sort of visualization of a data set, to capture Indigenous knowledge.
“You know in dramas, you see the person trying to unravel a mystery and they have the corkboard and the little notes and the string between them?” Mr. Yarlott said. “That’s basically what the IKG is, but for cultural knowledge.”
The first step was to gather the data. The team chose a culinary focus because it is a part of life that all people share. They collected recipes and related stories from both the public domain and team members.
Mr. Monteith chose to enter the story of the Three Sisters stew, a recipe created from symbiotic crops (corn, beans and squash) that he said is known among Indigenous peoples wherever those ingredients grow. The story of the Three Sisters, he said, is not only a recipe but a way to teach sustainability practices, such as the preservation of water. “It’s just a great metaphor for what we need to do as a society and as a people across the world,” Mr. Monteith said.
Using Neo4J, a graph database management system, the recipes were broken down into components (title, ingredients, instructions and related stories) and tagged with information, like the tribe of origin or whether the recipe was contemporary or historical, or had roots in folklore. This data set was then entered into Dialogflow, a natural language processing platform, so it could be fed into a chatbot — in this case, Sina Storyteller, the Siri-like conversational agent designed by IVOW. Currently, anyone can interact with the early version through Google Assistant.
The tools and techniques to create the IKG were designed to be basic enough that anyone, not just those with a background in computer science, could use them. And IKG uses only information that is widely available or that the team had permission to use from their own tribes, bands and nations.
There are challenges, though. The process is labor intensive and expensive; IVOW is a self-funded enterprise, and the work of the collaborators is voluntary.