Fine-grained relevance feedback for XML retrieval

Authors:
Hanglin Pan;Ralf Schenkel;Gerhard Weikum
Affiliations:
Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany
Venue:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2008

Citing 3
Cited 3

The transferable belief model

Artificial Intelligence
Using Dempster-Shafer's Theory of Evidence to Combine Aspects of Information Use

Journal of Intelligent Information Systems
TopX: efficient and versatile top-k query processing for semistructured data

The VLDB Journal — The International Journal on Very Large Data Bases

Exploring path query results through relevance feedback

Proceedings of the 18th ACM conference on Information and knowledge management
Feedback-driven result ranking and query refinement for exploring semi-structured data collections

Proceedings of the 13th International Conference on Extending Database Technology
Automatically incorporating new sources in keyword search-based data integration

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Quantified Score

Hi-index	0.00

Visualization

Abstract

This demonstration presents an XML IR system that allows users to give feedback of different granularities and types, using Dempster-Shafer theory of evidence to compute expanded and reweighted queries.