Search (177 results, page 3 of 9)

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Abstract

Document retrieval is a highly interactive process dealing with large amounts of information. Visual representations can provide both a means for managing the complexity of large information structures and an interface style well suited to interactive manipulation. The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval. A common visually displayed network structure is used for query, document content, and term relations. A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays. An associative thesaurus of terms and an inter-document network provide information about a document collection that can complement other retrieval aids. Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the inherent difficulties of orientation and navigation in large information structures.

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Abstract

While the search interfaces used by common academic digital libraries provide easy access to a wealth of peer-reviewed literature, their interfaces provide little support for exploratory browsing. When faced with a complex search task (such as one that requires knowledge discovery), exploratory browsing is an important first step in an exploratory search process. To more effectively support exploratory browsing, we have designed and implemented a novel academic digital library search interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. To study the potential value of these features, we have conducted a controlled laboratory study with 32 participants, comparing KLink Search to a baseline digital library search interface modeled after that used by IEEE Xplore. Based on subjective opinions, objective performance, and behavioral data, we show the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing.

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Abstract

This article explores the aesthetic design criteria that should be incorporated into the information visualization of a taxonomy intended for use by children. Seven elementary-school students were each asked to represent their ideas in drawings for visualizing a taxonomy. Their drawings were analyzed according to six criteria - balance, equilibrium, symmetry, unity, rhythm, and economy - identified as aesthetic measures in previous research. The drawings revealed the presence of all six measures, and three - unity, equilibrium, and rhythm - were found to play an especially important role. It is therefore concluded that an aesthetic design for an information visualization for young users should incorporate all six measures.

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Abstract

The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on three datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.

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Abstract

Conceptual modeling of bibliographic data, including the FR models and the consolidated IFLA LRM, has provided an opportunity to shift focus to entities and relationships and to support hierarchical work-based exploration of bibliographic information. This paper reports on a study examining the complexity of a work's bibliographic family data and user interactions with data visualizations, compared to traditional displays. Findings suggest that the FRBR-based visual bibliographic information system supports work families of different complexities more equally than a traditional system. Differences between the two systems also show that the FRBR-based system was more effective especially for related-works and author-related tasks.

Type

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Abstract

The discovery service (a search engine and service called WILBERT) used at our library at the Technical University of Applied Sciences Wildau (TUAS Wildau) is comprised of more than 8 million items. If we were to record all licensed publications in this tool to a higher level of articles, including their bibliographic records and full texts, we would have a holding estimated at a hundred million documents. A lot of features, such as ranking, autocompletion, multi-faceted classification, refining opportunities reduce the number of hits. However, it is not enough to give intuitive support for a systematic overview of topics related to documents in the library. John Naisbitt once said: "We are drowning in information, but starving for knowledge." This quote is still very true today. Two years ago, we started to develop micro thesauri for MINT topics in order to develop an advanced indexing of the library stock. We use iQvoc as a vocabulary management system to create the thesaurus. It provides an easy-to-use browser interface that builds a SKOS thesaurus in the background. The purpose of this is to integrate the thesauri in WILBERT in order to offer a better subject-related search. This approach especially supports first-year students by giving them the possibility to browse through a hierarchical alignment of a subject, for instance, logistics or computer science, and thereby discover how the terms are related. It also supports the students with an insight into established abbreviations and alternative labels. Students at the TUAS Wildau were involved in the developmental process of the software regarding the interface and functionality of iQvoc. The first steps have been taken and involve the inclusion of 3000 terms in our discovery tool WILBERT.

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

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Abstract

In this article and two other articles which conceptualize a future stage of the research program (Leide, Cole, Large, & Beheshti, submitted for publication; Cole, Leide, Large, Beheshti, & Brooks, in preparation), we map-out a domain novice user's encounter with an IR system from beginning to end so that appropriate classification-based visualization schemes can be inserted into the encounter process. This article describes the visualization of a navigation classification scheme only. The navigation classification scheme uses the metaphor of a ship and ship's navigator traveling through charted (but unknown to the user) waters, guided by a series of lighthouses. The lighthouses contain mediation interfaces linking the user to the information store through agents created for each. The user's agent is the cognitive model the user has of the information space, which the system encourages to evolve via interaction with the system's agent. The system's agent is an evolving classification scheme created by professional indexers to represent the structure of the information store. We propose a more systematic, multidimensional approach to creating evolving classification/indexing schemes, based on where the user is and what she is trying to do at that moment during the search session.

Type

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Abstract

Purpose - To describe how information visualization can be used in the design of interface tools for large-scale repositories. Design/methodology/approach - One challenge for designers in the context of large-scale repositories is to create interface tools that help users find specific information of interest. In order to be most effective, these tools need to leverage the cognitive characteristics of the target users. At the Los Alamos National Laboratory, the authors' target users are scientists and engineers who can be characterized as higher-order, analytical thinkers. In this paper, the authors describe a visualization tool they have created for making the authors' large-scale digital object repositories more usable for them: SearchGraph, which facilitates data set analysis by displaying search results in the form of a two- or three-dimensional interactive scatter plot. Findings - Using SearchGraph, users can view a condensed, abstract visualization of search results. They can view the same dataset from multiple perspectives by manipulating several display, sort, and filter options. Doing so allows them to see different patterns in the dataset. For example, they can apply a logarithmic transformation in order to create more scatter in a dense cluster of data points or they can apply filters in order to focus on a specific subset of data points. Originality/value - SearchGraph is a creative solution to the problem of how to design interface tools for large-scale repositories. It is particularly appropriate for the authors' target users, who are scientists and engineers. It extends the work of the first two authors on ActiveGraph, a read-write digital library visualization tool.

Type

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Abstract

Although information retrieval techniques used by Web search engines have improved substantially over the years, the results of Web searches have continued to be represented in simple list-based formats. Although the list-based representation makes it easy to evaluate a single document for relevance, it does not support the users in the broader tasks of manipulating or exploring the search results as they attempt to find a collection of relevant documents. HotMap is a meta-search system that provides a compact visual representation of Web search results at two levels of detail, and it supports interactive exploration via nested sorting of Web search results based on query term frequencies. An evaluation of the search results for a set of vague queries has shown that the re-sorted search results can provide a higher portion of relevant documents among the top search results. User studies show an increase in speed and effectiveness and a reduction in missed documents when comparing HotMap to the list-based representation used by Google. Subjective measures were positive, and users showed a preference for the HotMap interface. These results provide evidence for the utility of next-generation Web search results interfaces that promote interactive search results exploration.

Type

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Abstract

Based on real-world user demands, we demonstrate how animated visualization of evolving text corpora displays the underlying dynamics of semantic content. To interpret the results, one needs a dynamic theory of word meaning. We suggest that conceptual dynamics as the interaction between kinds of intellectual and emotional content and language is key for such a theory. We demonstrate our method by two-way seriation, which is a popular technique to analyze groups of similar instances and their features as well as the connections between the groups themselves. The two-way seriated data may be visualized as a two-dimensional heat map or as a three-dimensional landscape in which color codes or height correspond to the values in the matrix. In this article, we focus on two-way seriation of sparse data in the Reuters-21568 test collection. To achieve a meaningful visualization, we introduce a compactly supported convolution kernel similar to filter kernels used in image reconstruction and geostatistics. This filter populates the high-dimensional sparse space with values that interpolate nearby elements and provides insight into the clustering structure. We also extend two-way seriation to deal with online updates of both the row and column spaces and, combined with the convolution kernel, demonstrate a three-dimensional visualization of dynamics.

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Abstract

Purpose - Despite the importance of bibliographic information systems for discovering and exploring library resources, some of the core functionality that should be provided to support users in their information seeking process is still missing. Investigating these issues, the purpose of this paper is to design a solution that would fulfil the missing objectives. Design/methodology/approach - Building on the concepts of a work family, functional requirements for bibliographic records (FRBR) and information visualization, the paper proposes a model and user interface design that could support a more efficient and user-friendly presentation and navigation in bibliographic information systems. Findings - The proposed design brings together all versions of a work, related works, and other works by and about the author and shows how the model was implemented into a FrbrVis prototype system using hierarchical visualization layout. Research limitations/implications - Although issues related to discovery and exploration apply to various material types, the research first focused on works of fiction and was also limited by the selected sample of records. Practical implications - The model for presenting and interacting with FRBR-based data can serve as a good starting point for future developments and implementations. Originality/value - With FRBR concepts being gradually integrated into cataloguing rules, formats, and various bibliographic services, one of the important questions that has not really been investigated and studied is how the new type of data would be presented to users in a way that would exploit the true potential of the changes.

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Abstract

The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.

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Abstract

Visitors to museums and other cultural heritage sites encounter a wealth of exhibits in a variety of subject areas, but can explore only a small number of them. Moreover, there typically exists rich complementary information that can be delivered to the visitor about exhibits of interest, but only a fraction of this information can be consumed during the limited time of the visit. Recommender systems may help visitors to cope with this information overload. Ideally, the recommender system of choice should model user preferences, as well as background knowledge about the museum's environment, considering aspects of physical and thematic relevancy. We propose a personalized graph-based recommender framework, representing rating history and background multi-facet information jointly as a relational graph. A random walk measure is applied to rank available complementary multimedia presentations by their relevancy to a visitor's profile, integrating the various dimensions. We report the results of experiments conducted using authentic data collected at the Hecht museum. An evaluation of multiple graph variants, compared with several popular and state-of-the-art recommendation methods, indicates on advantages of the graph-based approach.

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Abstract

This investigation tested the designer assumption that VIBE is a tool for an expert user and asked: what are the effects of user expertise on usability when VIBE's non-traditional interface is compared with a more traditional text-based interface? Three user groups - novices, online searching experts, and VIBE system experts - totaling 31 participants, were asked to use and compare VIBE to a more traditional text-based system, askSam. No significant differences were found; however, significant performance differences were found for some tasks on the two systems. Participants understood the basic principles underlying VIBE although they generally favored the askSam system. The findings suggest that VIBE is a learnable system and its components have pragmatic application to the development of visualized information retrieval systems. Further research is recommended to maximize the retrieval potential of IR visualization systems.

Type

a

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Abstract

An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.

Type

a

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Abstract

The use of thesaurus-based indexing is a common approach for increasing the performance of information retrieval. In this thesis, we examine the suitability of a thesaurus for a given set of information and evaluate improvements of existing thesauri to get better search results. On this area, we focus on two aspects: 1. We demonstrate an analysis of the indexing results achieved by an automatic document indexer and the involved thesaurus. 2. We propose a method for thesaurus evaluation which is based on a combination of statistical measures and appropriate visualization techniques that support the detection of potential problems in a thesaurus. In this chapter, we give an overview of the context of our work. Next, we briefly outline the basics of thesaurus-based information retrieval and describe the Collexis Engine that was used for our experiments. In Chapter 3, we describe two experiments in automatically indexing documents in the areas of medicine and economics with corresponding thesauri and compare the results to available manual annotations. Chapter 4 describes methods for assessing thesauri and visualizing the result in terms of a treemap. We depict examples of interesting observations supported by the method and show that we actually find critical problems. We conclude with a discussion of open questions and future research in Chapter 5.

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Abstract

Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top-n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.

Type

a

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Abstract

We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

Type

a

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Abstract

This research takes the academic articles in the Web of Science's core collection database as a corpus to draw a series of knowledge maps, to explore the relationships, connectivity, dis-tribution, and evolution among their keywords with respect to smart cities in the last decade. Beyond just drawing a text cloud or measuring their sizes, we further explore their texture by iden-tifying the hottest keywords in academic articles, construct links between and among them that share common keywords, identify islands, rocks, reefs that are formed by connected articles-a metaphor inspired by Ong et al. (2005)-and analyze trends in their evolution. We found the following phenomena: 1) "Internet of Things" is the most frequently mentioned keyword in recent research articles; 2) the numbers of islands and reefs are increas-ing; 3) the evolutions of the numbers of weighted links have frac-tal-like structure; and, 4) the coverage of the largest rock, formed by articles that share a common keyword, in the largest island is converging into around 10% to 20%. These phenomena imply that a common interest in the technology of smart cities has been emerging among researchers. However, the administrative, social, economic, and cultural issues need more attention in academia in the future.

Type

a