Search (275 results, page 13 of 14)

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Abstract

Choi and Rasmussen collect queries to the Library of Congress's American Memory photo archive from 48 scholars in American History by way of interviews and pre and post search questionnaires. Their interest is in the types of information need common in the visual domain, and the categories of terms most often used or indicated as appropriate for the description of image contents. Each search resulted in the provision of 20 items for evaluation by the searcher. Terms in queries and acceptable retrievals were categorized by a who, what, when, where faceted classification and queries into four needs categories; specific, general, abstract, and subjective. Two out of three analysts assigned all 38 requests into the same one of the four categories and in 19 cases all three agreed. General/nameable needs accounted for 60.5%, specific needs 26.3%, 7.9% for general/abstract, and 5.3% for subjective needs. The facet analysis indicated most content was of the form person/thing or event/condition limited by geography or time.

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Abstract

This article explores the effects of undergraduate students' mental models of the Web on their online searching behavior. Forty-four undergraduate students, mainly freshmen and sophomores, participated in the study. Subjects' mental models of the Web were treated as equally good styles and operationalized as drawings of their perceptions about the Web. Four types of mental models of the Web were identified based on the drawings and the associated descriptions: technical view, functional view, process view, and connection view. In the study, subjects were required to finish two search tasks. Searching behavior was measured from four aspects: navigation and performance, subjects' feelings about tasks and their own performances, query construction, and search patterns. The four mental model groups showed different navigation and querying behaviors, but the differences were not significant. Subjects' satisfaction with their own performances was found to be significantly correlated with the time to complete the task. The results also showed that the familiarity of the task to subjects had a major effect on their ways to start interaction, query construction, and search patterns.

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Abstract

Key terms related to information searching and search models are defined. A historic context is provided to illustrate the evolution of the four main digital environments that users interact with in their search process to offer readers background information regarding the transition from manual information systems to computer-based information retrieval (IR) systems, as well as the transition from intermediary searching to end-user searching. Emphasis is placed on the review of different levels of information searching from search tactics/moves, search strategies, and usage patterns, to search models and associated factors in relation to task, user knowledge structure, IR system design, and social-organization context. Search models are further classified into two types, with one type illustrating information search process (ISP) and the other type emphasizing the factors that influence the process. In addition, unsolved problems and future research are discussed and suggested.

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Abstract

This paper presents QACID an ontology-based Question Answering system applied to the CInema Domain. This system allows users to retrieve information from formal ontologies by using as input queries formulated in natural language. The original characteristic of QACID is the strategy used to fill the gap between users' expressiveness and formal knowledge representation. This approach is based on collections of user queries and offers a simple adaptability to deal with multilingual capabilities, inter-domain portability and changes in user information requirements. All these capabilities permit developing Question Answering applications for actual users. This system has been developed and tested on the Spanish language and using an ontology modelling the cinema domain. The performance level achieved enables the use of the system in real environments.

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Abstract

Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.

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Abstract

Online trolling has become increasingly prevalent and visible in online communities. Perceptions of and reactions to trolling behaviors varies significantly from one community to another, as trolling behaviors are contextual and vary across platforms and communities. Through an examination of seven trolling scenarios, this article intends to answer the following questions: how do trolling behaviors differ across contexts; how do perceptions of trolling differ from case to case; and what aspects of context of trolling are perceived to be important by the public? Based on focus groups and interview data, we discuss the ways in which community norms and demographics, technological features of platforms, and community boundaries are perceived to impact trolling behaviors. Two major contributions of the study include a codebook to support future analysis of trolling and formal concept analysis surrounding contextual perceptions of trolling.

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Abstract

The purpose of this analysis was to evaluate an existing set of search tasks in terms of their effectiveness as part of a "shared infrastructure" for conducting interactive IR research. Twenty search tasks that varied in their cognitive complexity and domain were assigned to 47 study participants; the 3,101 moves used to complete those tasks were then analyzed in terms of frequency of each type of move and the sequential patterns they formed. The cognitive complexity of the tasks influenced the number of moves used to complete the tasks, with the most complex (i.e., Create) tasks requiring more moves than tasks at other levels of complexity. Across the four domains, the Commerce tasks elicited more search moves per search. When sequences of moves were analyzed, seven patterns were identified; some of these patterns were associated with particular task characteristics. The findings suggest that search tasks can be designed to elicit particular types of search behaviors and, thus, allow researchers to focus attention on particular aspects of IR interactions.

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Abstract

This study investigates the influence of individual and information characteristics on university students' information channel selection (that is, search engines, social question & answer sites, online health websites, and social networking sites) of online health information (OHI) for three different types of search tasks (factual, exploratory, and personal experience). Quantitative data were collected via an online questionnaire distributed to students on various postgraduate programs at a large UK university. In total, 291 responses were processed for descriptive statistics, Principal Component Analysis, and Poisson regression. Search engines are the most frequently used among the four channels of information discussed in this study. Credibility, ease of use, style, usefulness, and recommendation are the key factors influencing users' judgments of information characteristics (explaining over 62% of the variance). Poisson regression indicated that individuals' channel experience, age, student status, health status, and triangulation (comparing sources) as well as style, credibility, usefulness, and recommendation are substantive predictors for channel selection of OHI.

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Abstract

Based on the author's 1984 dissertation, this technical and detailed volume looks at problems related to control of terms used in searching among a variety of databases

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Abstract

Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.

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Abstract

Journalists were chosen for study because it was felt that they would be, as information seekers and packagers par excellence, in the advanced guard of Internet users and setting the pace. As it turned out this was not to be case. Despite what appear to be the considerable and direct benefits for them, after having interviewed approximately 150 journalists and observed the action in a variety of news rooms, it appears that less than one in five national journalist use the Internet and the proportion is much less than that for regional journalists. If this poor Internet take up in the workplace was unexpected, another surprise is the characteristics of those who have actually taken the Internet route. Far from being the stereotypical young and male, most are well practised journalist into their thirties/forties, which, of course, runs counter to all that we have been led to believe. Surprisingly, the study showed as much, if not more, interest in using the Internet from the supposedly `busy' senior managers and editors than in the rank and file.

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Abstract

Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation. Although studies have shown that the users of Web-based search engines typically enter short, term-based queries and rarely use search operators, little information exists concerning the effects of term and operator usage on the relevancy of search results. In this study, search engine users formulated queries on eight search topics. Each query was submitted to the user-specified search engine, and relevancy ratings for the retrieved pages were assigned. Expert-formulated queries were also submitted and provided a basis for comparing relevancy ratings across search engines. Data analysis based on our research model of the term and operator factors affecting relevancy was then conducted. The results show that the difference in the number of terms between expert and nonexpert searches, the percentage of matching terms between those searches, and the erroneous use of nonsupported operators in nonexpert searches explain most of the variation in the relevancy of search results. These findings highlight the need for designing search engine interfaces that provide greater support in the areas of term selection and operator usage

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Abstract

Although many studies have identified search tactics, few studies have explored tactic transitions. This study investigated the transitions of search tactics during the Web-based search process. Bringing their own 60 search tasks, 31 participants, representing the general public with different demographic characteristics, participated in the study. Data collected from search logs and verbal protocols were analyzed by applying both qualitative and quantitative methods. The findings of this study show that participants exhibited some unique Web search tactics. They overwhelmingly employed accessing and evaluating tactics; they used fewer tactics related to modifying search statements, monitoring the search process, organizing search results, and learning system features. The contributing factors behind applying most and least frequently employed search tactics are in relation to users' efforts, trust in information retrieval (IR) systems, preference, experience, and knowledge as well as limitation of the system design. A matrix of search-tactic transitions was created to show the probabilities of transitions from one tactic to another. By applying fifth-order Markov chain, the results also presented the most common search strategies representing patterns of tactic transition occurring at the beginning, middle, and ending phases within one search session. The results of this study generated detailed and useful guidance for IR system design to support the most frequently applied tactics and transitions, to reduce unnecessary transitions, and support transitions at different phases.

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Abstract

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.

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Abstract

Both user involvement and system support play important roles in applying search tactics. To apply search tactics in the information retrieval (IR) processes, users make decisions and take actions in the search process, while IR systems assist them by providing different system features. After analyzing 61 participants' information searching diaries and questionnaires we identified various types of user involvement and system support in applying different types of search tactics. Based on quantitative analysis, search tactics were classified into 3 groups: user-dominated, system-dominated, and balanced tactics. We further explored types of user involvement and types of system support in applying search tactics from the 3 groups. The findings show that users and systems play major roles in applying user-dominated and system-dominated tactics, respectively. When applying balanced tactics, users and systems must collaborate closely with each other. In this article, we propose a model that illustrates user involvement and system support as they occur in user-dominated tactics, system-dominated tactics, and balanced tactics. Most important, IR system design implications are discussed to facilitate effective and efficient applications of the 3 groups of search tactics.

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