A new search engine integrating hierarchical browsing and keyword search

Authors:
Da Kuang;Xiao Li;Charles X. Ling
Affiliations:
Department of Computer Science, The University of Western Ontario, London, Ontario, Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, Canada
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

Citing 19
Cited 0

An Evaluation of Statistical Approaches to Text Categorization

Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchical Text Classification and Evaluation

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A taxonomy of web search

ACM SIGIR Forum
ScentTrails: Integrating browsing and searching on the Web

ACM Transactions on Computer-Human Interaction (TOCHI)
An extensive empirical study of feature selection metrics for text classification

The Journal of Machine Learning Research
Learning to cluster web search results

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Findex: search result categories help users when document ranking fails

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A personalized search engine based on web-snippet hierarchical clustering

WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Support vector machines classification with a very large-scale taxonomy

ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Quantifying query ambiguity

HLT '02 Proceedings of the second international conference on Human Language Technology Research
The class imbalance problem: A systematic study

Intelligent Data Analysis
Deep classification in large-scale text hierarchies

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Decision trees for hierarchical multi-label classification

Machine Learning
LIBLINEAR: A Library for Large Linear Classification

The Journal of Machine Learning Research
Web page classification: Features and algorithms

ACM Computing Surveys (CSUR)
Survey and evaluation of query intent detection methods

Proceedings of the 2009 workshop on Web Search Click Data
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
A survey of hierarchical classification across different application domains

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i.e., not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on SupportVector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1.2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.