Learning to merge search results for efficient Distributed Information Retrieval

Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the results pages. By not downloading additional information, such as the full document, we decrease bandwidth usage. CORI and Round Robin merging were used as our baselines; surprisingly, our results show that the SVM-methods do not improve over those baselines

Updating collection representations for federated search

To facilitate the search for relevant information across a set of online distributed collections, a federated information retrieval system typically represents each collection, centrally, by a set of vocabularies or sampled documents. Accurate retrieval is therefore related to how precise each representation reflects the underlying content stored in that collection. As collections evolve over time, collection representations should also be updated to reflect any change, however, a current solution has not yet been proposed. In this study we examine both the implications of out-of-date representation sets on retrieval accuracy, as well as proposing three different policies for managing necessary updates. Each policy is evaluated on a testbed of forty-four dynamic collections over an eight-week period. Our findings show that out-of-date representations significantly degrade performance overtime, however, adopting a suitable update policy can minimise this problem

Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Query expansion with naive bayes for searching distributed collections

The proliferation of online information resources increases the importance of effective and efficient distributed searching. However, the problem of word mismatch seriously hurts the effectiveness of distributed information retrieval. Automatic query expansion has been suggested as a technique for dealing with the fundamental issue of word mismatch. In this paper, we propose a method - query expansion with Naive Bayes to address the problem, discuss its implementation in IISS system, and present experimental results demonstrating its effectiveness. Such technique not only enhances the discriminatory power of typical queries for choosing the right collections but also hence significantly improves retrieval results

Beyond English text: Multilingual and multimedia information retrieval.

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