Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Title language model for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
WebOQL: Restructuring Documents, Databases, and Webs
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Learning domain-independent string transformation weights for high accuracy object identification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SG-WRAP: A Schema-Guided Wrapper Generator
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Segmented document classification: problem and solution
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm -ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine-CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests.