Resource Selection for Federated Search on the Web

TL;DR

This paper introduces a new dataset and evaluates resource selection methods for federated web search, highlighting challenges like sparse descriptions and skewed collection sizes, and proposing size estimation techniques.

Contribution

It provides a new web federated search dataset, analyzes resource size estimation, and compares resource selection methods under real web conditions.

Findings

Size estimation improves resource selection accuracy

Smaller web search engines can effectively replace larger ones

Existing resource selection methods face challenges due to data sparsity and skewed sizes

Abstract

A publicly available dataset for federated search reflecting a real web environment has long been absent, making it difficult for researchers to test the validity of their federated search algorithms for the web setting. We present several experiments and analyses on resource selection on the web using a recently released test collection containing the results from more than a hundred real search engines, ranging from large general web search engines such as Google, Bing and Yahoo to small domain-specific engines. First, we experiment with estimating the size of uncooperative search engines on the web using query based sampling and propose a new method using the ClueWeb09 dataset. We find the size estimates to be highly effective in resource selection. Second, we show that an optimized federated search system based on smaller web search engines can be an alternative to a system using large web search engines. Third, we provide an empirical comparison of several popular resource selection methods and find that these methods are not readily suitable for resource selection on the web. Challenges include the sparse resource descriptions and extremely skewed sizes of collections.