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This study explored how the expression of search facets and relevance feedback (RF) by users was related to search success in interactive and automatic query expansion in the course of the search process. Search success was measured both in the number of relevant documents retrieved, whether identified by users or not. Research design consisted of 26 users searching for four TREC topics in Okapi IR system, half of the searchers using interactive and half automatic query expansion based on RF. The search logs were recorded, and the users filled in questionnaires for each topic concerning various features of searching. The results showed that the exhaustivity of the query was the most significant predictor of search success. Interactive expansion led to better search success than automatic expansion if all retrieved relevant items were counted, but there was no difference between the methods if only those items recognised relevant by users were observed. The analysis showed that the difference was facilitated by the liberal relevance criterion used in TREC not favouring highly relevant documents in evaluation.

The purpose of this paper is to establish a new approach for solving the expansion term problem.

This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.

The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.

The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.

Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.

Query expansion and query limitation are two known techniques for assisting users to define efficient queries. The purpose of this article is to examine the effectiveness of the two methods.

The research entailed an objective and subjective evaluation of the effectiveness of automatic and interactive query expansion and of two query limit options. The evaluation included both lab simulations and large‐scale user studies. The objective aspects were evaluated in lab simulations with experts judging user performance. The subjective analysis was carried out by having the participants evaluate the quality of, and express their satisfaction with, the retrieval process and its results, thus employing perceived‐value analysis.

The main findings reveal a difference between the perceived and real values of these techniques. While users expressed their satisfaction with interactive query expansion and its performance, the real‐value analysis of their performance did not show any significant difference between the retrieval modes.

The article evaluates the objective and subjective effectiveness of automatic and interactive query expansion and two query limit options.

The purpose of this paper is to explore query expansion via conceptual distance in thesaurus indexed collections

An extract of the National Museum of Science and Industry's collections database, indexed with the Getty Art and Architecture Thesaurus (AAT), was the dataset for the research. The system architecture and algorithms for semantic closeness and the matching function are outlined. Standalone and web interfaces are described and formative qualitative user studies are discussed. One user session is discussed in detail, together with a scenario based on a related public inquiry. Findings are set in context of the literature on thesaurus‐based query expansion. This paper discusses the potential of query expansion techniques using the semantic relationships in a faceted thesaurus.

Thesaurus‐assisted retrieval systems have potential for multi‐concept descriptors, permitting very precise queries and indexing. However, indexer and searcher may differ in terminology judgments and there may not be any exactly matching results. The integration of semantic closeness in the matching function permits ranked results for multi‐concept queries in thesaurus‐indexed applications. An in‐memory representation of the thesaurus semantic network allows a combination of automatic and interactive control of expansion and control of expansion on individual query terms.

The application of semantic expansion to browsing may be useful in interface options where thesaurus structure is hidden.

This paper presents a framework which combines data and text retrieval techniques to exercise and evaluate ontology based query expansions. We prepare by using linguistic techniques to identify query and document concepts, locating them in a ontologically defined semantic space. Expansions originate from the identified query concepts, with success determined by matching in the relevant document set. We identify three orthogonal dimensions that can affect query expansion success; relationship source, success measure technique, and query expansion technique. Expansion technique is further divided into six different categories: simple pruning, complex probability, voting, directional, semantic propagation, and multiple source concept. We describe each technique and show examples where they would be useful. The system architecture used facilitates plugging in of various expansion and evaluation routines, and flowing results from one method to the next. The system is useful for microanalysis of query expansion, discovering which components of ontological derived knowledge most influence query expansion success. In this work, we apply our framework to the medical domain.

This study explores how experts and novices in pedagogics expanded queries supported by the ERIC thesaurus, and how this was connected to the search effectiveness in an easy and a difficult search task. The expert group consisted of 15 undergraduates in pedagogy and the novice group of 15 students with no previous studies in this field. Their search logs were recorded and a pre‐ and post‐search interview was conducted. The results show that the number and type of terms selected from the thesaurus for expansion by experts improved search effectiveness, whereas there were no connections between the use of thesaurus and improvement of effectiveness among novices. The effectiveness of expansions varied between the facets of the queries. Thus, a vital condition for benefiting from a thesaurus in query expansion to improve search results is sufficient familiarity with the search topic. The results suggest also that it is not in the first place the number of terms used in expansion, but their type and quality that are crucial for search success.

An automatic query expansion (AQE) facility in an online catalogue was evaluated in an operational library setting. The Okapi experimental system had other features including: ranked output ‘best match’ keyword searching, automatic stemming, spelling normalisation and cross referencing as well as relevance feedback. A combination of transaction log analysis, search replays, questionnaires and interviews was used for data collection. Findings show that contrary to previous results, AQE was beneficial in a substantial number of searches. User intentions, the effectiveness of the ‘best match’ search and user interaction were identified as the main factors affecting the take‐up of the query expansion facility.

The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

The experimental results show that the method the authors proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

The experimental results show that the method we proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

To the best of the authors’ knowledge, this paper provides the first attempt to gather all the retrieved documents from limited accessible data source, and the efficiency and ease of implementation of the proposed solution make it feasible for practical applications. The method the authors proposed can also benefit the construction of web corpus.

With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion.

The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods.

Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level.

Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Conventional studies mainly classify a term’s appearance in the retrieved documents as either relevant or irrelevant for application. The purpose of this paper is to differentiate the term’s appearances in the retrieved documents in more detailed situations to generate relevance information and demonstrate the applicability of the derived information in combination with current methods of query expansion.

A method was designed first to utilize the derived information owing to term appearance differentiation within a conventional query expansion approach that has been proven as an effective technology in the enhancement of information retrieval. Then, an information retrieval system was developed to demonstrate the realization and sustain the study of the method. Formal tests were conducted to examine the distinguishing capability of the proposed information utilized in the method.

The experimental results show that substantial differences in performances can be achieved between the proposed method and the conventional query expansion method alone.

Since the proposed information resides at the bottom of the information hierarchy of relevance feedback, any technology regarding the application of relevance feedback information could consider the utilization of this piece of information.

The importance of the study is the disclosure of the applicability of the proposed information beyond current usage of term appearances in relevant/irrelevant documents and the initiation of a query expansion technology in the application of this information.