A medium-scale user study was carried out to investigate the usability of a concept-based query expansion support tool. The tool was fully integrated into the interface of an IR system, and designed to support the user by offering automatically generated concept hierarchies. Two types of hierarchies were compared with a baseline. Several observations were made as a result of the study: 1) the hierarchy is often accessed after an examination of the first page of search results; 2) accessing the hierarchies reduces the number of iterations and paging actions; 3) accessing the hierarchies increases the chance of finding relevant items more accurately than the baseline; 4) the hierarchical structure helps the users to handle a large number of concepts; and finally, 5) subjects were not aware of the difference between two types of hierarchies.

It has been well documented that web searchers have difficulties crafting queries to fulfill their information needs. In this work, we use a concept knowledge base generated from the ACM Computing Classification System to generate a query space that represents the query terms in relation to the concepts they describe and the other terms that are related to these concepts. A visual representation of this query space allows the user to interpret the relationships between their query terms and the query space. Interactive query refinement within this visual representation takes advantage of the user’s visual information processing abilities, and allows the user to choose terms that accurately represent their information need. A preview of the search results from Google provides the user with an indication of the current state of their query refinement process. This work allows the user to take an active role in the information retrieval process, supporting the fundamental shift from information retrieval systems to information retrieval support systems. 1.

I n t hi s paper w e s t udy t er m- based f eedback f or i nf or mat i on r etrieval in the language modeling approach. With term feedback auserdirectly judges the relevance of individual terms without interaction with feedback documents, taking full control of the query expansion process. We propose a cluster-based method for selecting terms to present to the user for judgment, as well as effective algorithms for constructing refined query language models from user term feedback. Our algorithms are shown to bring significant improvement in retrieval accuracy over a non-feedback baseline, and achieve comparable performance to relevance feedback. They are helpful even when there are no relevant documents in the top.

It is common for web searchers to have difficulties crafting queries to fulfill their information needs. Even when they provide a good query, users often find it challenging to evaluate the results of their web searches. Sources of these problems include the lack of support for query refinement, and the static nature of the list-based representations of web search results. To address these issues, we have developed WordBars, an interactive tool for web information retrieval. WordBars visually represents the frequencies of the terms found in the first 100 document surrogates returned from the initial query. This system allows the users to interactively re-sort the search results based on the frequencies of the selected terms within the document surrogates, as well as to add and remove terms from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when specific and vague initial queries are provided.

Abstract: In this paper, we present an ontology-based approach for the improvement of searching in an information portal. The approach is based on incremental refinement of user’s queries, according to the ambiguity of a query’s interpretation. The so-called Librarian Agent plays the role of the human librarian in the traditional library – it uses information, about the domain vocabulary, the capacity of the knowledge repository and the behaviour of previous users in order to help users find the resources they are interested in. Moreover, the agent analyses the users ’ requests off-line and compares the users ’ interests with the capacity of the information repository, in order to find which new topics should be introduced or which topics users are no more interested in. We partially implemented the approach in the Web Portal of our Institute and some initial evaluation results are shown.

Abstract. We show that eigenvector decomposition can be used to extract a term taxonomy from a given collection of text documents. So far, methods based on eigenvector decomposition, such as latent semantic indexing (LSI) or principal component analysis (PCA), were only known to be useful for extracting symmetric relations between terms. We give a precise mathematical criterion for distinguishing between four kinds of relations of a given pair of terms of a given collection: unrelated (car-fruit), symmetrically related (car- automobile), asymmetrically related with the first term being more specific than the second (banana- fruit), and asymmetrically related in the other direction (fruit- banana). We give theoretical evidence for the soundness of our criterion, by showing that in a simplified mathematical model the criterion does the apparently right thing. We applied our scheme to the reconstruction of a selected part of the open directory project (ODP) hierarchy, with promising results.

This paper describes FameIR, a software environment developed for providing IR capabilities, both indexing and retrieving, when information is represented in different media (text in various languages, audio, images, video, etc.). FameIR also allows access to information in Web pages and information services.

Our demonstration shows the hierarchy system working on a locally run search engine. Hierarchies are dynamically generated from the retrieved documents, and visualised on the menus. When a user selects a term from the hierarchy, the documents linked to the term are listed, and the term is then added to the initial query to rerun a search. Through the demonstration we illustrate how hierarchical presentation of expansion terms is achieved, and how our approach supports users to articulate their information needs using the hierarchy.

This paper addresses the problem of information fusion using a multi-agent information gathering system. We present a hierarchical subject-based query expansion method, followed by a cooperative fusion algorithm for unstructured documents. We evaluate the performance using the traditional methods of precision and recall. The results show that the subject-based fusion method is promising and efficient. 1.

Abstract not found