{"id":158529,"date":"2009-08-01T00:00:00","date_gmt":"2009-08-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/hamster-using-search-clicklogs-for-schema-and-taxonomy-matching\/"},"modified":"2018-10-16T20:22:10","modified_gmt":"2018-10-17T03:22:10","slug":"hamster-using-search-clicklogs-for-schema-and-taxonomy-matching","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/hamster-using-search-clicklogs-for-schema-and-taxonomy-matching\/","title":{"rendered":"HAMSTER: Using Search Clicklogs for Schema and Taxonomy Matching"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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\u2019s 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\u2019s 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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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\u2019s data warehouse. Our experiments show that traditional schemabased and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-158529","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Very Large Data Bases Endowment Inc.","msr_edition":"Proceedings of VLDB 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