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This study aims to explore a framework for evaluating and comparing two federated search tools (FSTs) using two different retrieval protocols: XML gateways and Z39.50. FSTs are meta-information retrieval systems developed to facilitate the searching of multiple resources through a single search box. FSTs allow searching of heterogeneous platforms, such as bibliographic and full-text databases, online public access catalogues, web search engines and open-access resources.

The proposed framework consists of three phases: the usability testing, retrievability performance assessment and overall comparison. The think-aloud protocol was implemented for usability testing and FSTs retrieval consistency, and precision tests were carried out to assess the retrievability performance for 20 real user queries.

Participants were directed to assign weights for the interface usability and system retrievability importance as indicators for FST evaluation. Results indicated that FSTs retrievability performance was of more importance than the interface usability. Participants assigned an average weight of 62 per cent for the system retrievability and 38 per cent for interface usability. In terms of the usability test, there was no significant difference between the two FSTs, while minor differences were found regarding retrieval consistency and precision at 11-point cut-off recall. The overall evaluation showed that the FST based on the XML gateway rated slightly higher than the FST based on the Z39.50 protocol.

This empirical study faced several limitations. First, the lack of participants’ familiarity with usability testing created the need for a deep awareness and rigorous supervision. Second, the difficulties of empirically assessing participants’ perspectives and future attitudes called for mixing between a formal task and the think-aloud protocol for participants in a real environment. This has been a challenge that faced the collection of the usability data including user behaviour, expectations and other empirical data. Third, the differences between the two FSTs in terms of number of connectors and advanced search techniques required setting rigorous procedures for testing FSTs retrieval consistency and precision.

This paper has practical implications in two dimensions. First, its results could be utilized by FST developers to enhance their product’s performance. Second, the framework could be used by librarians to evaluate FSTs performance and capabilities. The framework enables them to compare between library systems in general and FSTs in particular. In addition to these practical implications, the authors encourage researchers to use and enhance the proposed framework.

Librarians can use the proposed framework to empirically select an FST, involving users in the selection procedures of these information retrieval systems, so that it accords with users’ perspectives and attitudes and serves the community better.

The proposed framework could be considered a benchmark for FST evaluation.

In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues.

This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods.

The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments.

Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.

In vol. 6, 1976, of Advances in Librarianship, I published a review about relevance under the same title, without, of course, “Part I” in the title (Saracevic, 1976). [A substantively similar article was published in the Journal of the American Society for Information Science (Saracevic, 1975)]. I did not plan then to have another related review 30 years later—but things happen. The 1976 work “attempted to trace the evolution of thinking on relevance, a key notion in information science, [and] to provide a framework within which the widely dissonant ideas on relevance might be interpreted and related to one another” (ibid.: 338).

The purpose of this paper is to study the effects of the choice of database and data retrieval methods on the research performance of a number of selected Asian universities from 33 countries using two different indicators (publication volume and citation count) and three subject fields (energy, environment and materials) during the period 2005‐2009.

To determine the effect of the choice of database, Scopus and Web of Science databases were queried to retrieve the publications and citations of the top ten Asian universities in three subject fields. In ascertaining the effect of data retrieval methods, the authors proposed a new data retrieval method called Keyword‐based Data Retrieval (KDR), which uses relevant keywords identified by independent experts to retrieve publications and their citations of the top 30 Asian universities in the Environment field from the entire Scopus database. The results were then compared with those retrieved using the Conventional Data Retrieval (CDR) method.

The Asian university ranking order is strongly affected by the choice of database, indicator, and the data retrieval method used. The KDR method yields many more publications and citation counts than the CDR method, shows better understanding of the university ranking results, and retrieves publications and citations in source titles outside those classified by the database. Moreover the publications found by the KDR method have a multidisciplinary research focus.

The paper concludes that KDR is a more suitable methodology to retrieve data for measuring university research performance, particularly in an environment where universities are increasingly engaging in multidisciplinary research.

The purpose of this project was to develop a web‐based self‐training package for information retrieval using the distance education approach.

The package was developed using the distance education approach with STOU Plan, STOU Plan 2000 and GMS‐VU applied. The distance education model for the web‐based self‐training package was composed of five stages: identifying the learners, design of the package, production of the package, establishing the delivery system, and evaluation. The system development methodology was based on the system development lifecycle (SDLC) with a combination of waterfall, phased and prototyping approaches. There are several phase in SDLC to carry out: problem and objective identification, requirement determination, requirement analysis, package design, package implementation, delivery system and evaluation. Evaluation of the package was conducted in two phases: formative evaluation and summative evaluation using the focus group discussion method. Formative evaluation was conducted during the package development by experts in the field prior to the summative evaluation. The summative evaluation was conducted after the package development had been completed as a pilot study for field trial by target users, consisting of junior library staff and library users. All comments were reviewed and refined in terms of instructional content, design, overall opinion and learning progress before put on production.

The package consists of three main components: About the project, Study modules, References and further readings. Study modules, which is the most important component, consists of ten instructional modules focusing on information retrieval, and self‐assessment through pre‐test and post‐test. The package includes multimedia such as images and sound to attract learners during their learning session. The delivery mode for the self‐training package offers both online and off‐line modes. Online mode is offered when there is network facility and internet connection available, while offline mode is offered through CD‐ROM without requiring network and internet connection. The features and functions of both modes are identical. Moreover, print materials are also included as supplementary media.

Since the module is a self‐directed learning or self‐training tool in information retrieval it can be employed for junior library staff and library users; it provides a training tool for librarians to train library users and supports human resource and development to narrow digital divides and support the right to access information.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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.

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.

Nurses are constantly faced with information needs to keep up in the fast pace, ever‐changing state of health and nursing. Information that is available through advancing computer and technology is oftentimes difficult and cumbersome to learn and access. This is a case study of how three nurses used and evaluated three different types of information search databases in terms of three measure criteria. The results are useful in guiding nurses to use these information sources, as well as other database users with little or no experience in searching such systems. Insights from the study can also assist system designers and programmers in future planning and redesigns of these systems, in order to maximize use and expand the user base of these search systems.