Search (13 results, page 1 of 1)

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Abstract

This article presents part of phase 2 of a research project funded by the NSF-National Science Digital Library Project, which observed how academic users interact with the ScienceDirect information retrieval system for simulated class-related assignments. The ultimate goal of the project is twofold: (1) to find ways to improve science and engineering students' use of science e-journal systems; (2) to develop methods to measure user interaction behaviors. Process-tracing technique recorded participants' processes and interaction behaviors that are measurable; think-aloud protocol captured participants' affective and cognitive verbalizations; pre- and post-search questionnaires solicited demographic information, prior experience with the system, and comments. We explored possible relationships between affective feelings and cognitive behaviors. During search interactions both feelings and thoughts occurred frequently. Positive feelings were more common and were associated more often with thoughts about results. Negative feelings were associated more often with thoughts related to the system, search strategy, and task. Learning styles are also examined as a factor influencing behavior. Engineering graduate students with an assimilating learning style searched longer and paused less than those with a converging learning style. Further exploration of learning styles is suggested.

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Abstract

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.

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Abstract

This project analyzed 541,920 user queries submitted to and executed in an academic Website during a four-year period (May 1997 to May 2001) using a relational database. The purpose of the study is three-fold: (1) to understand Web users' query behavior; (2) to identify problems encountered by these Web users; (3) to develop appropriate techniques for optimization of query analysis and mining. The linguistic analyses focus an query structures, lexicon, and word associations using statistical measures such as Zipf distribution and mutual information. A data model with finest granularity is used for data storage and iterative analyses. Patterns and trends of querying behavior are identified and compared with previous studies.

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Abstract

In this paper, we report on the second part of an empirical study designed to observe how users interact with World Wide Web resources. Applying a holistic approach, the researches examine users' cognitive, affective, and physical behaviors during user-Web interaction in order to understand better the nature of information retrieval on the Web, the needs of Web users, and the problem-solving strategies Web users employ. From analyses and the participant verbalizations collected during monitored searches, the researchers developed a taxonomy of problem solving strategies. The coding scheme was developed based on a content analysis of the integrated process data. Information from triangulation follow-up with participants via anonymously completed questionnaires, the taxonomy, and analyses of search transcripts were collected to determine 1) what problems users encountered during the interaction and how users solved these problems; and 2) which problem-solving strategies Web users considered and selected for finding factual information. The focus of the coding was on the participants' cognitive, affective, and physical behaviors in response to the components of the problems encountered, which included problems of the following types: Web interfaces, users' mental models, and the Web information sources. Searching behavior and problem-solving patterns are described and interpreted within the relevant situational context and the problems users encountered are identified and analyzed. Both the problems users faced and their problem-solving approaches endeavored evidence a strong reliance on mental models of the features available on sites, the location of those features, and other interface design concepts

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Abstract

This article reports on the follow-up study of a two-part project designed to study the decision-making process underlying how academic researchers select documents retrieved from online databases, consult or read, and cite documents during a research project. The participants are 15 of the the 25 agricultural economics users who participated in the original study of document-selection conducted in 1992. They were interviewed about subsequent decisions on document considered relevant and selected in 1992, as well as documents cited in their written products but not in the original searches. Of particular interest in this article are the decision criteria and rules they apply to documents as they progress through the project. The first study in 1992 emphasized the selection processes and resulted in a document selection model; the 1995 study concentrates on the reading and citing decisions. The model derived from this project shows document use as a decision-making process with decisions occuring at 3 points or stages during a research project: selecting, reading, and citing. It is an expansion pf the document selection model developed in the 1992 study, ientifies more criteria, and clarifies the criteria and rules that are in use at each stage. The follow-up study not only found that all but one of the criteria identified in selection re-occur in connection with reading and citing decisions, but also identified 14 new criteria. It also found that decision rules applied in selection descisions are applied throughout the project

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Abstract

Eleven middle school children constructed hierarchical maps for two science categories selected from two Web directories, Yahooligans! and KidsClick! For each category, children constructed a pair of maps: one without links and one with links. Forty-tour maps were analyzed to identify similarities and differences. The structures of the maps were compared to the structures employed by the directories. Children were able to construct hierarchical maps and articulate the relationships among the concepts. At the global level (whole map), children's maps were not alike and did not match the structures of the Web directories. At the local levels (superordinate and subordinate), however, children shared similarities in the conceptual configurations, especially for the concrete concepts. For these concepts, substantial overlap was found between the children's structures and those employed in the directories. For the abstract concepts the configurations were diverse and did not match those in the directories. The findings of this study have impl!cations for design of systems that are more supportive of children's conceptual structures.

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Abstract

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.

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Abstract

How knowledge is organized in human memory is of interest to both information science and cognitive science. The current information retrieval (IR) systems can be improved if we understand which conceptual structures could facilitate users in information processing and seeking. This project examined twenty-two cognitive maps on ten research topics generated by ten experts and eleven non-experts. Experts were those who had completed a research project on the topic prior to participating in this study, while non-experts were from the same academic department who were familiar with the topic but had not conducted any in-depth research on it. A research topic can be represented by a vocabulary and the relationships among the terms in the vocabulary. A cognitive map visualizes the vocabulary and its configuration in a plane. We observed that experts did not generate the maps much faster than non-experts. Both experts and non-experts modified the given vocabulary by either adding or dropping terms. The dominant configuration for the maps was top-down, while five maps were orientated in left-right or radical structure (from a center). Experts tended to use problem-oriented approach to organize the vocabulary while non-experts often applied discipline-oriented hierarchical structure. Despite of many differences in vocabulary and structure by individuals, there are terms clustered in a similar ways across maps indicating an agreed-upon semantic closeness among these terms

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Abstract

This is Part II of an experimental study investigating students' perceptions of research and research paper anxiety. The study integrates quantitative and qualitative designs to collect complimentary data. The participants were students in four sections of an upper division undergraduate course on technical and professional writing during the fall of 1999. A survey instrument used the Critical Incident Technique to solicit writings in students' own words about a memorable past research and writing experience at the beginning of the semester and the current research and writing at the end of the semester. The quantitative part of the survey measured students' perceptions about research using a questionnaire with five-point Likert scale, and students' anxiety levels using a standard state anxiety test (STAI Y-1). The first article, Part 1, provides a detailed description of the experimental design and reports on quantitative results. This article reports on content analysis of students' writings about their experiences of the two research projects. Analysis of the data confirmed Kuhlthau's Information Search Process (ISP) model and revealed additional affective and cognitive aspects related to research and writing.

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Abstract

A multidimensional-scaling approach is used to analyze frequently used medical-topic terms in queries submitted to a Web-based consumer health information system. Based on a year-long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co-occurring query terms are analyzed. An overlap-coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co-occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co-occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus-keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query-term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.

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Abstract

Automatic extraction of large-scale and accurate drug-disease pairs from the medical literature plays an important role for drug repurposing. However, many existing extraction methods are mainly in a supervised manner. It is costly and time-consuming to manually label drug-disease pairs datasets. There are many drug-disease pairs buried in free text. In this work, we first leverage a pattern-based method to automatically extract drug-disease pairs with treatment and inducement relationships from free text. Then, to reflect a drug-disease relation, a network embedding algorithm is proposed to calculate the degree of correlation of a drug-disease pair. In the experiments, we use the method to extract treatment and inducement drug-disease pairs from 27 million medical abstracts and titles available on PubMed. We extract 138,318 unique treatment pairs and 75,396 unique inducement pairs. Our algorithm achieves a precision of 0.912 and a recall of 0.898 in extracting the frequent treatment drug-disease pairs, and a precision of 0.923 and a recall of 0.833 in extracting the frequent inducement drug-disease pairs. Besides, our proposed information network embedding algorithm can efficiently reflect the degree of correlation of drug-disease pairs. Our algorithm can achieve a precision of 0.802, a recall of 0.783 in the fine-grained evaluation of extracting frequent pairs.

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Abstract

Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing approaches exploit techniques such as statistical models or machine leaning algorithms to assess Wikipedia article quality. However, existing models do not provide satisfactory results. Furthermore, these models fail to adopt a comprehensive feature framework. In this article, we conduct an extensive survey of previous studies and summarize a comprehensive feature framework, including text statistics, writing style, readability, article structure, network, and editing history. Selected state-of-the-art deep-learning models, including the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTMs) network, CNN-LSTMs, bidirectional LSTMs, and stacked LSTMs, are applied to assess the quality of Wikipedia. A detailed comparison of deep-learning models is conducted with regard to different aspects: classification performance and training performance. We include an importance analysis of different features and feature sets to determine which features or feature sets are most effective in distinguishing Wikipedia article quality. This extensive experiment validates the effectiveness of the proposed model.