HAMSTER: Human Assisted Mapping of Schema & Taxonomies to Enhance Relevance

We address the problem of unsupervised matching of schema information from a large number of data sources into the schema of a data warehouse. The matching process is the first step of a framework to integrate data feeds from thirdparty data providers into a structured-search engine’s data warehouse. Our experiments show that traditional schemabased and instance-based schema matching methods fall short. We propose a new technique based on the search engine’s clicklogs. Two schema elements are matched if the distribution of keyword queries that cause click-throughs on their instances are similar. We present experiments on large commercial datasets that show the new technique has much better accuracy than traditional techniques.