A framework for corroborating answers from multiple web sources

作者:

Highlights:

摘要

Search engines are increasingly efficient at identifying the best sources for any given keyword query, and are often able to identify the answer within the sources. Unfortunately, many web sources are not trustworthy, because of erroneous, misleading, biased, or outdated information. In many cases, users are not satisfied with the results from any single source. In this paper, we propose a framework to aggregate query results from different sources in order to save users the hassle of individually checking query-related web sites to corroborate answers. To return the best answers to the users, we assign a score to each individual answer by taking into account the number, relevance and originality of the sources reporting the answer, as well as the prominence of the answer within the sources, and aggregate the scores of similar answers. We conducted extensive qualitative and quantitative experiments of our corroboration techniques on queries extracted from the TREC Question Answering track and from a log of real web search engine queries. Our results show that taking into account the quality of web pages and answers extracted from the pages in a corroborative way results in the identification of a correct answer for a majority of queries.

论文关键词:Corroboration,Mean reciprocal rank,Precision,TREC,Web search

论文评审过程:Received 3 August 2010, Accepted 27 August 2010, Available online 3 September 2010.

论文官网地址:https://doi.org/10.1016/j.is.2010.08.008