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The purpose of this study is to carry out a systematic literature review for evidence-based assessment of the effectiveness of thesaurus in digital information retrieval systems. It also aimed to identify the evaluation methods, evaluation measures and data collection tools which may be used in evaluating digital information retrieval systems.

A systematic literature review (SLR) of 344 publications from LISA and 238 from Scopus has been carried out to identify the evaluation studies for analysis, and 15 evaluation studies have been analyzed.

This study presents evidences for the effectiveness of thesaurus in digital information retrieval systems. Various methods for evaluating digital information systems have been identified. Also, a wide range of evaluation measures and data collection tools have been identified.

The study was limited to the literature published in English language and indexed in LISA and Scopus. The evaluation methods, evaluation measures and data collection tools identified in this study may be used to design more cognizant evaluation studies for digital information retrieval systems.

The findings have significant implications for the administrators of any type of digital information retrieval systems in making more informed decisions toward implementation of thesaurus in resource description and access to digital collections.

This study extends our knowledge on the potentials of thesauri in digital information retrieval systems. It also provides cues for designing more cognizant evaluation studies for digital information systems.

The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is.

The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall.

Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others.

The study only takes into consideration basic image search feature, i.e. text-based search.

The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use.

The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.

Purpose — The overall quality of an information retrieval system depends on many different aspects of the system and its users' information seeking behaviour, such as the speed of the system, the user interface, the query language and the features provided by the engine. One of the most important aspects is the effectiveness of the retrieval system, i.e. its ability to retrieve items that are relevant to the information need of an end user. This chapter focuses on methods for measuring effectiveness, in particular focusing on recent work that more directly models the utility of an engine to its users.

Methodology/approach — We discuss traditional approaches to effectiveness evaluation based on test collections, then transition to approaches based on test collections along with explicit models of user interaction with search results. We contrast this with approaches for which the user is ‘in the loop’, such as user studies and online evaluations.

Research limitations/implications — If it were possible to model users perfectly, we could directly estimate the utility of a search engine to its users; this would undoubtedly have a transformative effect on information retrieval and web search research. In practice, this goal will never be achievable because users exhibit far too much variability in how they approach the search engine, and furthermore provide valuable feedback that models and simulations cannot provide. Nevertheless, better models of user interaction will help develop better web search engines for a wider variety of tasks more rapidly.

Originality/value of paper — This is the first work that surveys recent work on user model-based evaluation and places it in a context with traditional evaluation based on the Cranfield paradigm.

The purpose was to evaluate the effectiveness of Google (as an international search engine) as well as of Parsijoo, Rismoon, and Yooz (as Persian search engines).

In this research, Google search engine as an international search engine, and three local ones, Parsijoo, Rismoon, and Yooz, were selected for evaluation. Likewise, 32 subject headings were selected from the Persian Subject Headings List, and then simulated work tasks were assigned based on them. A total of 192 students from Ferdowsi University of Mashhad were asked to search for the information needed for simulated work tasks in the selected search engines, and then to copy the relevant website URLs in the search form.

The findings indicated that Google, Parsijoo, Rismoon, and Yooz had a significant difference in the precision, recall, and normalized discounted cumulative gain. There was also a significant difference in the effectiveness (average of precision, recall, and NDCG) of these four search engines in the retrieval of the Persian resources.

Users using an efficient search engine will attain more relevant documents, and Google search engine was more efficient in retrieving the Persian resources. It is recommended to use Google as it has a more efficient search.

In this research, for the first time, Google has been compared with local Persian search engines considering the new approach (simulated work tasks).

Current approaches to text retrieval based on indexing by words or index terms and on retrieving by specifying a Boolean combination of keywords are well known, as are their limitations. Statistical approaches to retrieval, as exemplified in commercial products like STATUS/IQ and Personal Librarian, are slightly better but still have their own weaknesses. Approaches to the indexing and retrieval of text based on techniques of automatic natural language processing (NLP) may soon start to realise their undoubted potential in terms of improving the quality and effectiveness of information retrieval. In this article we will explore what that potential is. We will divide information retrieval functionality into conceptual and traditional information retrieval and we will examine some of the current attempts at using various NLP techniques in both the indexing and retrieval operations.

This paper is concerned with recent work in the theory of information retrieval. More particularly, it is concerned with theories which tackle the problem of retrieval performance, in a sense which will be explained. The aim is not an exhaustive survey of such work; rather it is an analysis and synthesis of those contributions which I feel to be important or find interesting.

The purpose of this paper is to highlight the retrieval effectiveness of search engines taking into consideration both precision and relative recall.

The study is based on search engines that are selected on the basis of Alexa (Actionable Analytics for the web) Rank. Alexa listed top 500 sites, namely, search engines, portals, directories, social networking sites, networking tools, etc. But the scope of study is confined to only general search engines on the basis of language which was confined to English. Therefore only two general search engines are selected for the study . Alexa reports Google.com as the most visited website worldwide and Yahoo.com as the fourth most visited website globally. A total of 15 queries were selected randomly from PG students of Department of Library and Information Science during a period of eight days (from May 8 to May 15, 2014) which are classified manually into navigational, informational and transactional queries. However, queries are largely distributed on the two selected search engines to check their retrieval effectiveness as a training data set in order to define some characteristics of each type. Each query was submitted to the selected search engines which retrieved a large number of results but only the first 30 results were evaluated to limit the study in view of the fact that most of the users usually look up under the first hits of a query.

The study estimated the precision and relative recall of Google and Yahoo. Queries using concepts in the field of Library and Information Science were tested and were divided into navigational queries, informational queries and transactional queries. Results of the study showed that the mean precision of Google was high with (1.10) followed by Yahoo with (0.88). While as, mean relative recall of Google was high with (0.68) followed by Yahoo with (0.31), respectively.

The study highlights the retrieval effectiveness of only two search engines.

The research work is authentic and does not contain any plagiarized work.

The development of a given discipline in science and technology often depends on the availability of theories capable of describing the processes which control the field and of modelling the interactions between these processes. The absence of an accepted theory of information retrieval has been blamed for the relative disorder and the lack of technical advances in the area. The main mathematical approaches to information retrieval are examined in this study, including both algebraic and probabilistic models, and the difficulties which impede the formalization of information retrieval processes are described. A number of developments are covered where new theoretical understandings have directly led to the improvement of retrieval techniques and operations.

The nature and relative merits of two measures of information retrieval system effectiveness, documents read and relevant references retrieved, are discussed. Using data on 2,380 searches completed at London University's Central Information Service, the author presents a mathematical model for the relationship between these two variables. Data suggest that this is best modelled by a monotonically asymptotic function, i.e. by a function increasing to a limit. A negative exponential function provides the best fit to the data, though a simpler hyperbolic function also provides a good fit. The suggested relationship between documents read and relevant references retrieved is further confirmed when the data is analysed by subject area of the user group. The major distinction between groups was the limiting value for mean number of documents read. Medical scientists read considerably more than the average, while engineers read considerably less. Biological, physical and social scientists were indistinguishable from the population as a whole within the statistical limitations of the data. Potential areas for further research are suggested.

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.