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Paper short abstract:

We discuss issues arising from applying natural language processing and data science methods to search and analyse the collection of ethnography curated by the Human Relations Area Files, Yale University. In particular we examine how comparative research might be better enabled and pitfalls avoided.

Paper long abstract:

We discuss issues arising from applying natural language processing and data science methods to assist search and analysis of the largest online collection of ethnography, curated by the Human Relations Area Files (HRAF) at Yale University. In particular, we examine how comparative research might be better enabled and pitfalls avoided, and how eHRAF, and other online resources, can assume some level of interoperability so that research and practitioner communities can combine and utilise online data tools from different sources. iKLEWS (Infrastructure for Knowledge Linkages from Ethnography of World Societies) is a HRAF project funded by the US National Science Foundation. iKLEWS is developing semantic infrastructure and associated computer services for a growing textual database of ethnography (eHRAF World Cultures), presently with roughly 750,000 pages from 6,500 ethnographic documents covering 360 world societies over time. The basic goal is to greatly expand the value of eHRAF World Cultures to students and researchers who seek to understand the range of possibilities for human understanding, knowledge, belief and behaviour, including research for real-world problems we face today, such as: climate change; violence; disasters; epidemics; hunger; and war. Understanding how and why cultures vary in the range of possible outcomes in similar circumstances is critical to improving policy, applied science, and basic scientific understandings of the human condition in an increasingly globalised world. Moreover, seeing how others have addressed issues in the recent past can help us find solutions we might not find otherwise.