Search

Discusses the emergence of general audience digital libraries (GADLs), a context that represents a hybrid of both traditional information retrieval, using primarily bibliographic resources provided by database vendors, and popular information retrieval such as public search systems on the Web. Reports findings of a study investigating end-user searching and response to a GADL. (Author/LRW)

Investigates the influence of index term distributions and indexing exhaustivity on the document space within a visual information retrieval environment called DARE (Distance Angle Retrieval Environment). Discusses results that demonstrate the importance of term distribution and exhaustivity on the density of document spaces and their implications for retrieval, particularly with different term weighting schemes. (Author/LRW)

Reports findings from a study of searching behavior by over 200,000 users of the Excite search engine. Analysis of over one million queries revealed most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. Concludes that Web searching by the public differs significantly from searching of other information retrieval systems. (Contains 16 references.) (AEF)

Discusses information visualization techniques and introduces a visual term discrimination value analysis method using a document density space within a distance-angle-based visual information retrieval environment. Explains that applications of these methods facilitate more effective assignment of term weights to index terms within documents and may assist searchers in selecting search terms. (Author/LRW)

Investigated the impact of term frequencies, term distributions, and indexing exhaustivity on the representation of document spaces in a visual, vector-based retrieval environment. Compares document-space densities and discusses visualization techniques, term weights, and implications for automatic indexing in information retrieval systems. (Author/LRW)

Describes a study that compared a hypertext-based bibliographic retrieval system with a traditional Boolean-based system. Results, conducted with novice and experienced searchers, revealed that the Boolean system performed better for tasks with a large number of relevant items, and that searcher experience level did not play a significant role. (Author/LRW)

Although hypertext system usage has been studied, little research has examined the relationship of searcher performance and perception of performance, particularly for hypertext-based information retrieval systems for bibliographic data. This article reports preliminary findings of a study at the University of Wisconsin-Milwaukee in which 83 subjects using HyperLynx, a prototype hypertext bibliographic retrieval system. (PEN)

Outlines the file structure requirements for a microcomputer-based information retrieval system using FoxPro, a relational database management system (DBMS). Issues relating to the design and implementation of such systems are discussed, and two possible designs are examined in terms of space economy and practicality of implementation. (15 references) (LRW)

This second report on a two-part study on the application of informetrics to information retrieval (IR) system design used models of database contents in a factorial simulation study at the University of Western Ontario. The study explored whether different file structures were better suited for different informetric environments. (32 references) (LRW)

Examines how informetric characteristics of information retrieval (IR) system databases can be used to help system designers decide what type of file structures would provide the best performance. The development of appropriate models describing database contents is highlighted in this first part of a two-part study. (30 references) (LRW)

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation—where data references are included alongside bibliographic references in the reference section of a publication—is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined. [ABSTRACT FROM AUTHOR]

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map. [ABSTRACT FROM AUTHOR]

The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas. [ABSTRACT FROM AUTHOR]

Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: “hit and run” sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from “hit and run” sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help. [ABSTRACT FROM AUTHOR]

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods. [ABSTRACT FROM AUTHOR]

Two large data sets of queries submitted to the Excite search service were used to derive different distributions of query and search behaviours (query-term usage across queries, terms used per query, page requests per query, queries per user/ session, query frequency across users). The authors fitted each data set to theoretical models used in Informetric research, in additional to the standard Zipf model to which most frequency distributions of Internet usage phenomena are informally fitted. The generalized inverse Gaussian-Poisson distribution provided the best fits for the models tested. With more accurate models to represent observed Web usage behaviours, researchers will be better able to simulate user behaviours. [ABSTRACT FROM AUTHOR]

Presents a visual term discrimination value analysis method which uses a density space within a distance-angle-based visual information retrieval environment. Information visualization; Term discrimination capacity analysis; Term density space analysis; Advantages of the method.

The influence of sample size on informetric characteristics is examined to determine whether theoretical mathematical models can adequately fit large data sets. Two large data sets of queries submitted to the Excite search service were sampled for search characteristics (term frequencies, terms used per query, pages viewed per query, queries submitted per session) producing data sets of various sizes that were fitted to theoretical models to determine how the sample may influence a model's goodness-of-fit. Although theoretical models could adequately fit smaller data sets of up to 5000 observations in some cases, larger data sets could not be satisfactorily fitted using several goodness-of-fit techniques. Investigators must take into account that sample size does influence goodness-of-fit outcomes. The nature of the data and not the limitations of given goodness-of-fit tests results in significant outcomes. Such goodness-of-fit tests should be used for comparative purposes, rather than significance testing. [ABSTRACT FROM AUTHOR]