Information Retrieval Models for Distributed Collections
Project responsable Jacques Savoy
Kilian Stoffel
Team member Yves Rasolofo
Abstract We have investigated two major issues in Distributed Information Retrieval (DIR), namely: collection selection and search results merging. While most published works on these two issues are based on pre-stored metadata, the approaches described in this paper involve extracting the required information at the time the query isprocessed. In order to predict the relevance of collections to a given query, we analyse a limited number of full documents (e.g., the top five documents) retrieved from each collection and then consider term proximity within them. On the other hand, our merging technique is rather simple since input only requires document scores and lengths of results lists. Our experiments evaluate the retrieval effectiveness of these approaches and compare them with centralised indexing and various other DIR techniques (e.g., CORI [2][3][23]).
We conducted our experiments using two testbeds: one containing news articles extracted from four different sources (2 GB) and another containing 10 GB of Web pages. Our evaluations demonstrate that the retrieval effectiveness of our simple approaches is worth considering.
Keywords Information retrieval, machine learning, distributed IR, digital libraries, uncertain reasoning
Type of project Fundamental research project
Research area Information retrieval
Method of financing FNS
Status Completed
Start of project 1-4-2000
End of project 28-2-2003
Overall budget 120389
Contact Paul Cotofrei