Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial
communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers
to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance
search result quality as well as to reduce network overhead. Recently, researches focus on methods based
on query-oriented routing indices. These methods utilize the historical information of past queries and
query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a
peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set
of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is
selected from the current peer's local knowledge base thus a broadcast search is investigated which badly
affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers
having similar interests. It exploits not only the current peer's knowledge base but also that of the others in
the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval
effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited
peers number. Experimental results show that our approach improves the recall and precision metrics
while reducing dramatically messages traffic.
%0 Journal Article
%1 noauthororeditor2011query
%A Taoufik Yeferny, Amel Bouzeghoub
%A Arour, Khedija
%D 2011
%J International Journal of Advanced Information Technology (IJAIT)
%K information technology
%N 6
%P 1-11
%R 10.5121/ijait.2011.1601
%T A QUERY LEARNING ROUTING APPROACH BASED
ON SEMANTIC CLUSTERS
%U http://airccse.org/journal/IJAIT/papers/1211ijait01.pdf
%V 1
%X Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial
communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers
to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance
search result quality as well as to reduce network overhead. Recently, researches focus on methods based
on query-oriented routing indices. These methods utilize the historical information of past queries and
query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a
peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set
of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is
selected from the current peer's local knowledge base thus a broadcast search is investigated which badly
affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers
having similar interests. It exploits not only the current peer's knowledge base but also that of the others in
the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval
effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited
peers number. Experimental results show that our approach improves the recall and precision metrics
while reducing dramatically messages traffic.
@article{noauthororeditor2011query,
abstract = {Peer-to-peer systems have recently a remarkable success in the social, academic, and commercial
communities. A fundamental problem in Peer-to-Peer systems is how to efficiently locate appropriate peers
to answer a specific query (Query Routing Problem). A lot of approaches have been carried out to enhance
search result quality as well as to reduce network overhead. Recently, researches focus on methods based
on query-oriented routing indices. These methods utilize the historical information of past queries and
query hits to build a local knowledge base per peer, which represents the user's interests or profile. When a
peer forwards a given query, it evaluates the query against its local knowledge base in order to select a set
of relevant peers to whom the query will be routed. Usually, an insufficient number of relevant peers is
selected from the current peer's local knowledge base thus a broadcast search is investigated which badly
affects the approach efficiency. To tackle this problem, we introduce a novel method that clusters peers
having similar interests. It exploits not only the current peer's knowledge base but also that of the others in
the cluster to extract relevant peers. We implemented the proposed approach, and tested (i) its retrieval
effectiveness in terms of recall and precision, (ii) its search cost in terms of messages traffic and visited
peers number. Experimental results show that our approach improves the recall and precision metrics
while reducing dramatically messages traffic. },
added-at = {2018-08-09T11:24:23.000+0200},
author = {Taoufik Yeferny, Amel Bouzeghoub and Arour, Khedija},
biburl = {https://www.bibsonomy.org/bibtex/2074a36b828eeafbbec2187a0dc464d86/ijaitislive},
doi = {10.5121/ijait.2011.1601},
interhash = {2fdf18cc7fdfe76f17323de17e300327},
intrahash = {074a36b828eeafbbec2187a0dc464d86},
journal = {International Journal of Advanced Information Technology (IJAIT)},
keywords = {information technology},
month = {December},
number = 6,
pages = {1-11},
timestamp = {2018-08-09T11:24:23.000+0200},
title = {A QUERY LEARNING ROUTING APPROACH BASED
ON SEMANTIC CLUSTERS},
url = {http://airccse.org/journal/IJAIT/papers/1211ijait01.pdf},
volume = 1,
year = 2011
}