A framework for corroborating answers from multiple web sources

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 Unfortunately, many web sources are not trustworthy, because of erroneous, misleading, biased, or outdated information.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.

Research Highlights
► We propose a framework for corroborating answers from different sources..
► We assign scores to answers considering the relevance and originality of the source.
► We propose a top-k algorithm to efficiently retrieve pages for answer corroboration.
► We evaluate our approach using dataset from both TREC and MSN query logs.
► Our method improves answer quality (MRR value) over frequency-based approach by 16%.