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.
To integrate schema information from numerous structured data sources, Nandi and Bernstein REF proposed a semi-supervised mapping method relying on a log of queries that cause click-throughs.
HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching
PVLDB
PVLDB
Computer Science