Paper Info
Title
Rapid Synthesis of Domain-Specific Web Search Engines Based on Semi-Automatic Training-Example Generation
Abstract
In this paper, we propose two kinds of semi-automatic training-example generation algorithms for rapidly synthesizing a domain-specific Web search engine. We use the keyword spice model, as a basic framework, which is an excellent approach for building a domain-specific search engine with high precision and high recall. The keyword spice model, however, requires a huge amount of training examples which should be classified by hand. For overcoming this problem, we propose two kinds of refinement algorithms based on semi-automatic training-example generation: (i) the sample decision tree based approach, and (ii) the similarity based approach. These approaches make it possible to build a highly accurate domain-specific search engine with a little time and effort. The experimental results show that our approaches are very effective and practical for the personalization of a general-purpose search engine.
Year
DOI
Venue
2006
10.1109/WI.2006.143
Web Intelligence
Keywords
Field
DocType
semi-automatic training-example generation,domain-specific search engine,high recall,high precision,general-purpose search engine,rapid synthesis,semi-automatic training-example generation algorithm,accurate domain-specific search engine,domain-specific web search engine,keyword spice model,excellent approach,domain-specific web search,information retrieval,decision trees,search engines,web search engine,learning artificial intelligence,decision tree,generic algorithm,search engine
Web search engine,Data mining,Decision tree,Search engine,Information retrieval,Computer science,Spice,Artificial intelligence,Search analytics,Machine learning,Personalization
Conference
ISBN
Citations 
PageRank 
0-7695-2747-7
5
0.44
References 
Authors
6
4
Name
Order
Citations
PageRank
Hidetomo Nabeshima115414.88
Reiko Miyagawa250.44
Yuki Suzuki351.12
Koji Iwanuma413817.65