Machine learning applications in anthropology: Automated discovery over kinship structures

Abstract

A common problem in anthropological field work is generalizing rules governing social interactions and relations (particularly kinship) from a series of examples. One class of machine learning algorithms is particularly well-suited to this task: inductive logic programming systems, as exemplified by FOIL. A knowledge base of relationships among individuals is established, in the form of a series of single-predicate facts. Given a set of positive and negative examples of a new relationship, the machine learning programs build a Horn clause description of the target relationship. The power of these algorithms to derive complex hypotheses is demonstrated for a set of kinship relationships drawn from the anthropological literature. FOIL extends the capabilities of earlier anthropology-specific learning programs by providing a more powerful representation for induced relationships, and is better able to learn in the face of noisy or incomplete data.