Andres Marenco graduates on federated search

Federated Aggregated Search

by Andrés Marenco Zúñiga

The traditional search engine paradigm has changed from retrieving simple text documents, to selecting a broader combination of diverse document types (i.e. images, videos, maps…) that could satisfy the user’s information need. Each type of document, stored in specialized databases known as ‘verticals’, and found in either local or federated locations, is nowadays integrated into ‘aggregated search engines’. Due to this domain coverage of each vertical, when a query enters the system, only the ones which are most likely to contain the desired information should be selected. To perform this selection, a text representation of each vertical is created by directly sampling a set of documents from the vertical’s search engine. However, many times the vertical representation is not descriptive enough. Reasons such as the heterogeneous nature of the documents or the lack of cooperation of the vertical could negatively affect the generation of the representation. Thus, we focus on the problem of creating an aggregated search engine which integrates federated collections in an uncooperative environment. With the help of Wikipedia as a complementary external source of information, we investigate the use of three techniques found in the literature aimed to enrich the vertical representation: a) using only Wikipedia articles as representation; b) using a combination of Wikipedia articles and the sample obtained from the vertical; and c) expanding the contents of each sampled document. We discovered how by applying latent Dirichlet allocation to model the hidden topics of documents directly sampled from each vertical it is possible to identify Wikipedia articles with the same theme coverage as the vertical. Then, we demonstrate how by using only Wikipedia articles for representation of some particular verticals, the selection task is improved. As a second point, we explored the use of the modelled topics together with Wikipedia categories to boost the score of the verticals that could be associated with the query string. Although in this case our results are inconclusive, the experiments suggest that by applying query classification and then matching obtained categories with the verticals’ categories it is possible to increase the effectiveness of the vertical selection task.

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