Towards breaking the quality curse.: a web-querying approach to web people search.

  • Authors:

  • Dmitri V. Kalashnikov;Rabia Nuray-Turan;Sharad Mehrotra

  • Affiliations:

  • University of California, Irvine, Irvine, CA, USA;University of California, Irvine, Irvine, CA, USA;University of California, Irvine, Irvine, CA, USA

  • Venue:
  • Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2008

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Abstract

Searching for people on the Web is one of the most common query types to the web search engines today. However, when a person name is queried, the returned webpages often contain documents related to several distinct namesakes who have the queried name. The task of disambiguating and finding the webpages related to the specific person of interest is left to the user. Many Web People Search (WePS) approaches have been developed recently that attempt to automate this disambiguation process. Nevertheless, the disambiguation quality of these techniques leaves a major room for improvement. This paper presents a new server-side WePS approach. It is based on collecting co-occurrence information from theWeb and thus it uses theWeb as an external data source. A skyline-based classification technique is developed for classifying the collected co-occurrence information in order to make clustering decisions. The clustering technique is specifically designed to (a) handle the dominance that exists in data and (b) to adapt to a given clustering quality measure. These properties allow the framework to get a major advantage in terms of result quality over all the latest WePS techniques we are aware of, including all the 18 methods covered in the recent WePS competition [2].