LaSEWeb: Automating Search Strategies Over Semi-structured Web Data

We show how to programmatically model processes that humans use when extracting answers to queries (e.g., “Who invented typewriter?”, “List of Washington national parks”) from semi-structured Web pages returned by a search engine. This modeling enables various applications including automating repetitive search tasks, and helping search engine developers design micro-segments of factoid questions.

We describe the design and implementation of a domain-specific language that enables extracting data from a webpage based on its structure, visual layout, and linguistic patterns. We also describe an algorithm to rank multiple answers extracted from multiple webpages.

On 100,000+ queries (across 7 micro-segments) obtained from Bing logs, our system LaSEWeb answered queries with an average recall of 71%. Also, the desired answer(s) were present in top-3 suggestions for 95%+ cases.