We develop a method for predicting query performance by computing the relative entropy between a query language model and the corresponding collection language model. The resulting clarity score measures the coherence of the language usage in documents whose models are likely to generate the query. We suggest that clarity scores measure the ambiguity of a query with respect to a collection of documents and show that they correlate positively with average precision in a variety of TREC test sets. Thus, the clarity score may be used to identify ineffective queries, on average, without relevance information. We develop an algorithm for automatically setting the clarity score threshold between predicted poorly-performing queries and acceptable queries and validate it using TREC data. In particular, we compare the automatic thresholds to optimum thresholds and also check how frequently results as good are achieved in sampling experiments that randomly assign queries to the two classes.

Queries to search engines on the Web are usually short. They do not provide sufficient evidence for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to

Usually, a Digital Library (DL) is an information resource where users may submit queries to satisfy their daily information need. The CYCLADES system envisages a DL additionally as a personalized collaborative working and meeting space of people sharing common interests, where users (i) may organize the information space according to their own subjective view; (ii) may build communities, (iii) may become aware of each other, (iv) may exchange information and knowledge with other users, and (v) may get recommendations based on preference patterns of users. In this paper, we describe the CYCLADES system, show how users may define their own collections of records in terms of un-materialized views over the information space and how the system manages them. In particular, we show how the system automatically detects the archives where to search in, which are relevant to each user defined collection.

Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search e#ectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the e#ectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our

We present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a documentcentric approach to decide whether a posting for a given term should remain in the index or not. The decision is made based on the term's contribution to the document's Kullback-Leibler divergence from the text collection's global language model. Our technique can be used to decrease the size of the index by over 90%, at only a minor decrease in retrieval e#ectiveness. It thus allows us to make the index small enough to fit entirely into the main memory of a single PC, even for large text collections containing millions of documents. This results in great e#ciency gains, superior to those of earlier pruning methods, and an average response time around 20 ms on the GOV2 document collection.

Abstract. Query expansion is a well-known method for improving average effectiveness in information retrieval. However, the most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing queryevaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. 1

Many Web information services utilize techniques of information extraction (IE) to collect important facts from the Web. To create more advanced services, one possible method is to discover thematic information from the collected facts through text classification. However, most conventional text classification techniques rely on manual-labelled corpora and are thus ill-suited to cooperate with Web information services with open domains. In this work, we present a system named LiveClassifier that can automatically train classifiers through Web corpora based on user-defined topic hierarchies. Due to its flexibility and convenience, LiveClassifier can be easily adapted for various purposes. New Web information services can be created to fully exploit it; human users can use it to create classifiers for their personal applications. The effectiveness of classifiers created by LiveClassifier is well supported by empirical evidence.

Ontologies play a key role in Semantic Web research. A common use of ontologies in Semantic Web is to enrich the current Web resources with some well-defined meaning to enhance the search capabilities of existing web searching systems. This paper reports on how ontologies developed in the EU Semantic Web project SPIRIT are used to support retrieval of documents that are considered to be spatially relevant to users' queries. The query expansion techniques presented in this paper are based on both a domain and a geographical ontology.

The effectiveness of queries in information retrieval can be improved through query expansion. This technique automatically introduces additional query terms that are statistically likely to match documents on the intended topic. However, query expansion techniques rely on fixed parameters. Our investigation of the effect of varying these parameters shows that the strategy of using fixed values is questionable.

Personalization has been deemed one of the major challenges in information retrieval with a significant potential for providing better search experience to individual users. Especially, the need for enhanced user models better capturing elements such as users ’ goals, tasks, and contexts has been identified. In this paper, we introduce a statistical language model for user tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics. We propose a personalization framework that selectively matches the actual user information need with relevant past user tasks, and allows to dynamically switch the course of personalization from re-finding very precise information to biasing results to general user interests. In the extreme, our model is able to detect when the user’s search and browse history is not appropriate for aiding the user in satisfying her current information quest. Instead of blindly applying personalization to all user queries, our approach refrains from undue actions in these cases, accounting for the user’s desire of discovering new topics, and changing interests over time. The effectiveness of our method is demonstrated by an empirical user study.