Indigenous frameworks for data-intensive humanities: recalibrating the past through knowledge engineering and generative modelling.

Abstract

Identifying, contacting and engaging missing shareholders constitutes an enormous challenge for Māori incorporations, iwi and hapū across Aotearoa New Zealand. Without accurate data or tools to har-monise existing fragmented or conflicting data sources, issues around land succession, opportunities for economic development, and maintenance of whānau relationships are all negatively impacted. This unique three-way research collaboration between Victoria University of Wellington (VUW), Parininihi ki Waitotara Incorporation (PKW), and University of Auckland funded by the National Science Challenge | Science for Technological Innovation catalyses innovation through new digital humanities-inflected data science modelling and analytics with the kaupapa of reconnecting missing Māori shareholders for a prosperous economic, cultural, and socially revitalised future. This paper provides an overview of VUW's culturally-embedded social network approach to the project, discusses the challenges of working within an indigenous worldview, and emphasises the importance of decolonising digital humanities.

Keywords

• indigenous knowledge
• semantic web
• generative modelling
• bayesian record linkage
• network analysis
• [info.info-lg]computer science [cs]/machine learning [cs.lg]
• [shs.hist]humanities and social sciences/history
• [shs.museo]humanities and social sciences/cultural heritage and museology