DOCTORAL ABSTRACT

Federated Search in Heterogeneous
Environments
Jaime ArguelloSchool of Information and Library Science
University of North Carolina
Chapel Hill, NC 27599
USAjarguello@unc.edu
2011
Abstract
In information retrieval, federated search is the problem of automatically searching acrossmultiple distributed collections or resources. It is typically decomposed into two subsequent
steps: deciding which resources to search (resource selection) and deciding how to combine
results from multiple resources into a single presentation (results merging). Federated searchoccurs in different environments. This dissertation focuses on an environment that has notbeen deeply investigated in prior work.
The growing heterogeneity of digital media and the broad range of user informationneeds that occur in today’s world have given rise to a multitude of systems that specialize
on a specific type of search task. Examples include search for news, images, video, local
businesses, items for sale, and even social-media interactions. In the Web search domain,these specialized systems are called verticals and one important task for the Web searchengine is the prediction and integration of relevant vertical content into the Web search
results. This is known as aggregated web search and is the main focus on this dissertation.
Providing a single-point of access to all these diverse systems requires federated searchsolutions that can support result-type and retrieval-algorithm heterogeneity. This type ofheterogeneity violates major assumptions made by state-of-the-art resource selection andresults merging methods and motivates the development of new techniques.
While existing resource selection methods derive evidence exclusively from sampled resource content, the approaches proposed in this dissertation draw on machine learning asa means to easily integrate various different types of evidence. These include, for example, evidence derived from (sampled) vertical content, vertical query-traffic, click-throughinformation, and properties of the query string. In order to operate in a heterogeneous environment, we focus on methods that can learn a vertical-specific relationship between featuresand relevance. We also present methods that reduce the need for human-produced training
data. In particular, we focus on the situation where we have vertical-relevance judgments forsome verticals and want to learn a predictive model for a vertical associated with no training
data.

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Existing results merging methods formulate the task as score normalization. In a moreheterogeneous environment, however, combining results into a single presentation requires
satisfying a number of layout constraints. The dissertation proposes a novel formulationof the task: block ranking. During block-ranking, the objective is to rank sequences of results that must appear grouped together (vertically or horizontally) in the final presentation.
Based on this formulation, the dissertation proposes and empirically validates a cost-effective
methodology for evaluating aggregated web search results. Finally, it proposes the use ofmachine learning methods for the task of block-ranking.
This dissertation was completed at School of Computer Science at Carnegie Mellon University under the advise of Dr. Jamie Callan (dissertation committee chair). Dr. Jaime Carbonell, Dr. Yiming Yang, and Dr. Fernando Diaz served as dissertation committee members.
For the full dissertation, visit: http://ils.unc.edu/~jarguell

ACM SIGIR Forum79

Vol. 46 No. 1 June 2012