  As the web expands in data volume and in geographical distribution,
centralized search methods become inefficient, leading to increasing interest
in cooperative information retrieval, e.g., federated text retrieval (FTR).
Different from existing centralized information retrieval (IR) methods, in
which search is done on a logically centralized document collection, FTR is
composed of a number of peers, each of which is a complete search engine by
itself. To process a query, FTR requires firstly the identification of
promising peers that host the relevant documents and secondly the retrieval of
the most relevant documents from the selected peers. Most of the existing
methods only apply traditional IR techniques that treat each text collection as
a single large document and utilize term matching to rank the collections. In
this paper, we formalize the problem and identify the properties of FTR, and
analyze the feasibility of extending LSI with clustering to adapt to FTR, based
on which a novel approach called Cluster-based Distributed Latent Semantic
Indexing (C-DLSI) is proposed. C-DLSI distinguishes the topics of a peer with
clustering, captures the local LSI spaces within the clusters, and consider the
relations among these LSI spaces, thus providing more precise characterization
of the peer. Accordingly, novel descriptors of the peers and a compatible local
text retrieval are proposed. The experimental results show that C-DLSI
outperforms existing methods.
