... This paper presents query-based sampling, a new technique for acquiring accurate resource descriptions. Query-based sampling does not require the cooperationof resource providers nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are created, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic database selection.

A multi-database model of distributed information retrieval is presented, in which people are assumed to have access to many searchable text databases. In such an environment, full-text information retrieval consists of discovering database contents, ranking databases by their expected ability to satisfy the query, searching a small number of databases, and merging results returned by different databases. This paper presents algorithms for each task. It also discusses how to reorganize conventional test collections into multi-database testbeds, and evaluation methodologies for multi-database experiments. A broad and diverse group of experimental results is presented to demonstrate that the algorithms are effective, efficient, robust, and scalable. 1. INTRODUCTION Wide area networks, particularly the Internet, have transformed how people interact with information. Much of the routine information access by the general public is now based on full-text information retrieval, as opposed t...

This paper evaluates the retrieval effectiveness of distributed information retrieval systems in realistic environments. We find that when a large number of collections are available, the retrieval effectiveness is significantly worse than that of centralized systems, mainly because typical queries are not adequate for the purpose of choosing the right collections. We propose two techniques to address the problem. One is to use phrase information in the collection selection index and the other is query expansion. Both techniques enhance the discriminatory power of typical queries for choosing the right collections and hence significantly improve retrieval results. Query expansion, in particular, brings the effectiveness of searching a large set of distributed collections close to that of searching a centralized collection. 1 Introduction In today's network environments, information is highly distributed. The Internet or World Wide Web, for example, contains thousands of collections. ...

We compare the performance of two database selection algorithms reported in the literature. Their performance is compared using a common testbed designed specifically for database selection techniques. The testbed is a decomposition of the TREC/- TIPSTER data into 236 subcollections. We present results of a recent investigation of the performance of the CORI algorithm and compare the performance with earlier work that examined the performance of gGlOSS. The databases from our testbed were ranked using both the gGlOSS and CORI techniques and compared to the RBR baseline, a baseline derived from TREC relevance judgements. We examined the degree to which CORI and gGlOSS approximate this baseline. Our results confirm our earlier observation that the gGlOSS Ideal(l) ranks do not estimate relevance- This work supported in part by DARPA contract N6600197 -C-8542 and NASA GSRP NGT5-50062. y This work supported in part by NSF, the Library of Congress, and the Department of Commerce under agre...

Many valuable text databases on the web have non-crawlable contents that are “hidden ” behind search interfaces. Metasearchers are helpful tools for searching over many such databases at once through a unified query interface. A critical task for a metasearcher to process a query efficiently and effectively is the selection of the most promising databases for the query, a task that typically relies on statistical summaries of the database contents. Unfortunately, web-accessible text databases do not generally export content summaries. In this paper, we present an algorithm to derive content summaries from “uncooperative ” databases by using “focused query probes,” which adaptively zoom in on and extract documents that are representative of the topic coverage of the databases. Our content summaries are the first to include absolute document frequency estimates for the database words. We also present a novel database selection algorithm that exploits both the extracted content summaries and a hierarchical classification of the databases, automatically derived during probing, to compensate for potentially incomplete content summaries. Finally, we evaluate our techniques thoroughly using a variety of databases, including 50 real web-accessible text databases. Our experiments indicate that our new content-summary construction technique is efficient and produces more accurate summaries than those from previously proposed strategies. Also, our hierarchical database selection algorithm exhibits significantly higher precision than its flat counterparts. 1

We evaluate server selection methods in a Web environment, modeling a digital library which makes use of existing Web search servers rather than building its own index. The evaluation framework portrays the Web realistically in several ways. Its search servers index real Web documents, are of various sizes, cover different topic areas and employ different retrieval methods. Selection is based on statistics extracted from the results of probe queries submitted to each server. We evaluate published selection methods and a new method for enhancing selection based on expected search server effectiveness. Results show CORI to be the most effective of three published selection methods. CORI selection steadily degrades with fewer probe queries, causing a drop in early precision of as much as 0#05 (one relevant document out of 20). Modifying CORI selection based on an estimation of expected effectiveness disappointingly yields no significant improvement in effectiveness. However, modifying COR...

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts -- database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step.

Collection selection has been a research issue for years. Typically, in related work, precomputed statistics are employed in order to estimate the expected result quality of each collection, and subsequently the collections are ranked accordingly. Our thesis is that this simple approach is insufficient for several applications in which the collections typically overlap. This is the case, for example, for the collections built by autonomous peers crawling the web. We argue for the extension of existing quality measures using estimators of mutual overlap among collections and present experiments in which this combination outperforms CORI, a popular approach based on quality estimation. We outline our prototype implementation of a P2P web search engine, coined MINERVA 1, that allows handling large amounts of data in a distributed and self-organizing manner. We conduct experiments which show that taking overlap into account during collection selection can drastically decrease the number of collections that have to be contacted in order to reach a satisfactory level of recall, which is a great step toward the feasibility of distributed web search.

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users’ search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user’s search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user’s search intention and serve as a context to disambiguate the words in the user’s query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

This article presents a semisupervised learning solution to the result merging problem. The key contribution is the observation that information used to create resource descriptions for resource selection can also be used to create a centralized sample database to guide the normalization of document scores returned by different databases. At retrieval time, the query is sent to the selected databases, which return database-specific document scores, and to a centralized sample database, which returns database-independent document scores. Documents that have both a database-specific score and a database-independent score serve as training data for learning to normalize the scores of other documents. An extensive set of experiments demonstrates that this method is more effective than the well-known CORI result-merging algorithm under a variety of conditions