A cognitive model of human bias in matching

Abstract

The schema matching problem is at the basis of integrating structured and semi-structured data. Being investigated in the fields of databases, AI, semantic Web and data mining for many years, the core challenge still remains the ability to create quality matchers, automatic tools for identifying correspondences among data concepts (e.g., database attributes). In this work, we investigate human matchers behavior using a new concept termed match consistency and introduce a novel use of cognitive models to explain human matcher performance. Using empirical evidence, we further show that human matching suffers from predictable biases when matching schemata, which prevent them from providing consistent matching.