Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Effective retrieval with distributed collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Methods for information server selection
ACM Transactions on Information Systems (TOIS)
Comparing the performance of database selection algorithms
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
The effectiveness of query expansion for distributed information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A local search mechanism for peer-to-peer networks
Proceedings of the eleventh international conference on Information and knowledge management
Pruning long documents for distributed information retrieval
Proceedings of the eleventh international conference on Information and knowledge management
NeuroGrid: Semantically Routing Queries in Peer-to-Peer Networks
Revised Papers from the NETWORKING 2002 Workshops on Web Engineering and Peer-to-Peer Computing
PlanetP: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
Content-based retrieval in hybrid peer-to-peer networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Improving collection selection with overlap awareness in P2P search engines
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Query-driven document partitioning and collection selection
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Emerging semantic communities in peer web search
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
Federated text retrieval from uncooperative overlapped collections
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Global term weights in distributed environments
Information Processing and Management: an International Journal
Database selection and result merging in P2P web search
DBISP2P'05/06 Proceedings of the 2005/2006 international conference on Databases, information systems, and peer-to-peer computing
An evaluation measure for distributed information retrieval systems
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Semantic social overlay networks
IEEE Journal on Selected Areas in Communications
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This paper experimentally studies approaches to the problem of ranking information resources w.r.t. user queries in peer-to-peer information retrieval. In distributed environments, for each given user query and a set of information resources that are available, we need to select the right subset of these resources to forward the query to. Here, we study the problem of pruning descriptions of resources to acceptable lengths in a peer-to-peer scenario and two approaches to overcome the mismatch problem that may arise as a consequence of the pruning, namely query expansion and learning better resource descriptions from query streams. The results show that resource descriptions can be pruned to a large extent without ill effects and that learning better descriptions from query streams works much better than query expansion.