Collection profiling for collection fusion in distributed information retrieval systems

Authors:
Chengye Lu;Yue Xu;Shlomo Geva
Affiliations:
School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, QLD, Australia;School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, QLD, Australia;School of Software Engineering and Data Communications, Queensland University of Technology, Brisbane, QLD, Australia
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

Citing 8
Cited 0

The effectiveness of GIOSS for the text database discovery problem

SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Searching distributed collections with inference networks

SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
STARTS: Stanford proposal for Internet meta-searching

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Query-based sampling of text databases

ACM Transactions on Information Systems (TOIS)
Approaches to collection selection and results merging for distributed information retrieval

Proceedings of the tenth international conference on Information and knowledge management
Using sampled data and regression to merge search engine results

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Web Based Collection Selection Using Singular Value Decomposition

WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalizing scores based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database and do not consider the retrieval performance. In this paper, we address the problem that in peer to peer information systems and argue that the performance of search engine should also be considered. We also proposed a collection profiling strategy which can discover not only collection content but also retrieval performance. Web-based query classification and two collection fusion approaches based on the collection profiling are also introduced in this paper. Our experiments show that our merging strategies are effective in merging results on uncooperative environment.