Query expansion describes the automated process of supplementing a user’s search with additional terms or geographic locations to make it more appropriate for the user's needs. Such process relies on the system’s knowledge about the relation between geographic terms and places. Geo

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word

Internet search engines have popularized the keywordbased search paradigm. While traditional database management systems offer powerful query languages, they do not allow keyword-based search. In this paper, we discuss DBXplorer, a system that enables keywordbased search in relational databases

suggest that expanding query vectors with words that are lexically related to the original query words can ameliorate some of the problems of mismatched vocabularies. This paper examines the utility of lexical query expansion in the large, diverse TREC collection. Concepts are represented by Word

search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank

jects that use these technologies for archiving and retrieval. The main goal of the MAESTRO project is to discover, implement, and evaluate various combinations of these technologies to achieve analysis performance that surpasses the sum of the parts. For example, British Prime Minister Tony Blair can be identified in the news by his voice, his appearance, captions, and other cues. A combination of these cues should provide more reliable identification of the Prime Minister than using any of the cues on their own. MAESTRO is a highly multidisciplinary effort, involving contributions from three laboratories across two divisions at SRI. Each of these SRI technologies is described in more detail here. The integrating architecture makes it easy to combine these in different ways, and to incorporate new analysis technologies developed by our team or by others. Multimedia Analysis Technologies on the MAESTRO Score On the MAESTRO Score (see Figure 1), on each line, similar

Abstract Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via odhoc feedback improves the re-trieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used

defined bounds on the number of returned hits. We assume a real-world setting where the user is not given direct access to a Web search engine’s index, i.e., querying is possible only through an interface. The goal to be optimized is the overall number of submitted Web queries. One original

given ranked position. We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (In

The problem of Web search, as traditionally formulated, has a very “pure” motivation: it seeks to take the content people produce on the Web and find the pages that are most relevant, useful, or authoritative for any given query. However, it soon became clear that a lucrative market existed