A Supervised Learning Algorithm for Binary Domain Classification of Web Queries using SERPs

TL;DR

This paper presents a supervised learning method that classifies web queries into scholar or non-scholar domains by analyzing features from SERPs, improving routing to specialized digital libraries.

Contribution

The study introduces a novel approach using SERP features for domain classification of queries, with high accuracy on large, real-world datasets.

Findings

Achieved a precision of 0.809 in classifying queries.

F-measure of 0.805 demonstrates effective performance.

Utilized large datasets from AOL and NASA for training and evaluation.

Abstract

General purpose Search Engines (SEs) crawl all domains (e.g., Sports, News, Entertainment) of the Web, but sometimes the informational need of a query is restricted to a particular domain (e.g., Medical). We leverage the work of SEs as part of our effort to route domain specific queries to local Digital Libraries (DLs). SEs are often used even if they are not the "best" source for certain types of queries. Rather than tell users to "use this DL for this kind of query", we intend to automatically detect when a query could be better served by a local DL (such as a private, access-controlled DL that is not crawlable via SEs). This is not an easy task because Web queries are short, ambiguous, and there is lack of quality labeled training data (or it is expensive to create). To detect queries that should be routed to local, specialized DLs, we first send the queries to Google and then examine the features in the resulting Search Engine Result Pages (SERPs), and then classify the query as belonging to either the scholar or non-scholar domain. Using 400,000 AOL queries for the non-scholar domain and 400,000 queries from the NASA Technical Report Server (NTRS) for the scholar domain, our classifier achieved a precision of 0.809 and F-measure of 0.805.