Annotation of URLs: more than the sum of parts

Authors:
Max Hinne;Wessel Kraaij;Stephan Raaijmakers;Suzan Verberne;Theo van der Weide;Maarten van der Heijden
Affiliations:
Radboud University, Nijmegen, Netherlands;Radboud University, Nijmegen, Netherlands;TNO, Delft, Netherlands;Radboud University, Nijmegen, Netherlands;Radboud University, Nijmegen, Netherlands;Radboud University, Nijmegen, Netherlands
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

Citing 7
Cited 2

BoosTexter: A Boosting-based Systemfor Text Categorization

Machine Learning - Special issue on information retrieval
Evaluating implicit measures to improve web search

ACM Transactions on Information Systems (TOIS)
Fast webpage classification using URL features

Proceedings of the 14th ACM international conference on Information and knowledge management
Query-sets: using implicit feedback and query patterns to organize web documents

Proceedings of the 17th international conference on World Wide Web
Clustering the tagged web

Proceedings of the Second ACM International Conference on Web Search and Data Mining
Survey and evaluation of query intent detection methods

Proceedings of the 2009 workshop on Web Search Click Data
Using query logs and click data to create improved document descriptions

Proceedings of the 2009 workshop on Web Search Click Data

Extracting user profiles from large scale data

Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
G-WSTD: a framework for geographic web search topic discovery

Proceedings of the 21st ACM international conference on Information and knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently a number of studies have demonstrated that search engine logfiles are an important resource to determine the relevance relation between URLs and query terms. We hypothesized that the queries associated with a URL could also be presented as useful URL metadata in a search engine result list, e.g. for helping to determine the semantic category of a URL. We evaluated this hypothesis by a classification experiment based on the DMOZ dataset. Our method can also annotate URLs that have no associated queries.