Exploiting Locality in Searching the Web

TL;DR

This paper reexamines claims that using locality can significantly improve web search efficiency by conducting experiments with a standardized framework and dataset, providing insights into the validity of previous results.

Contribution

It introduces a reproducible experimental framework and tools to evaluate locality-based web search strategies using the WT10g corpus, clarifying prior claims.

Findings

Locality-based search can improve efficiency under certain conditions.

Experimental results support some earlier claims but also highlight limitations.

The framework enables consistent replication and further research.

Abstract

Published experiments on spidering the Web suggest that, given training data in the form of a (relatively small) subgraph of the Web containing a subset of a selected class of target pages, it is possible to conduct a directed search and find additional target pages significantly faster (with fewer page retrievals) than by performing a blind or uninformed random or systematic search, e.g., breadth-first search. If true, this claim motivates a number of practical applications. Unfortunately, these experiments were carried out in specialized domains or under conditions that are difficult to replicate. We present and apply an experimental framework designed to reexamine and resolve the basic claims of the earlier work, so that the supporting experiments can be replicated and built upon. We provide high-performance tools for building experimental spiders, make use of the ground truth and static nature of the WT10g TREC Web corpus, and rely on simple well understand machine learning techniques to conduct our experiments. In this paper, we describe the basic framework, motivate the experimental design, and report on our findings supporting and qualifying the conclusions of the earlier research.