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Some of the main ranking features of today's search engines reflect resultpopularity and are based on ranking models, such as PageRank, implicit feedbackaggregation, and more. While such features yield satisfactory results for awide range of queries, they aggravate the problem of search for ambiguousentities: Searching for a person yields satisfactory results only if the personwe are looking for is represented by a high-ranked Web page and all requiredinformation are contained in this page. Otherwise, the user has to eitherreformulate/refine the query or manually inspect low-ranked results to find theperson in question. A possible approach to solve this problem is to cluster theresults, so that each cluster represents one of the persons occurring in theanswer set. However clustering search results has proven to be a difficultendeavor by itself, where the clusters are typically of moderate quality.  A wealth of useful information about persons occurs in Web 2.0 platforms,such as LinkedIn, Wikipedia, Facebook, etc. Being human-generated, theinformation on these platforms is clean, focused, and already disambiguated. Weshow that when searching for ambiguous person names the information from suchplatforms can be bootstrapped to group the results according to the individualsoccurring in them. We have evaluated our methods on a hand-labeled dataset ofaround 5,000 Web pages retrieved from Google queries on 50 ambiguous personnames.

Bootstrapped Grouping of Results to Ambiguous Person Name Queries