Search (67 results, page 2 of 4)

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Abstract

A PICTURE is said to be worth a thousand words. That metaphor might be expected to pertain a fortiori in the case of scientific papers, where a figure can brilliantly illuminate an idea that might otherwise be baffling. Papers with figures in them should thus be easier to grasp than those without. They should therefore reach larger audiences and, in turn, be more influential simply by virtue of being more widely read. But are they?

Content

Bill Howe and his colleagues at the University of Washington, in Seattle, decided to find out. First, they trained a computer algorithm to distinguish between various sorts of figures-which they defined as diagrams, equations, photographs, plots (such as bar charts and scatter graphs) and tables. They exposed their algorithm to between 400 and 600 images of each of these types of figure until it could distinguish them with an accuracy greater than 90%. Then they set it loose on the more-than-650,000 papers (containing more than 10m figures) stored on PubMed Central, an online archive of biomedical-research articles. To measure each paper's influence, they calculated its article-level Eigenfactor score-a modified version of the PageRank algorithm Google uses to provide the most relevant results for internet searches. Eigenfactor scoring gives a better measure than simply noting the number of times a paper is cited elsewhere, because it weights citations by their influence. A citation in a paper that is itself highly cited is worth more than one in a paper that is not.
As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.
Dr Howe and his colleagues do, however, believe that the study of diagrams can result in new insights. A figure showing new metabolic pathways in a cell, for example, may summarise hundreds of experiments. Since illustrations can convey important scientific concepts in this way, they think that browsing through related figures from different papers may help researchers come up with new theories. As Dr Howe puts it, "the unit of scientific currency is closer to the figure than to the paper." With this thought in mind, the team have created a website (viziometrics.org (http://viziometrics.org/) ) where the millions of images sorted by their program can be searched using key words. Their next plan is to extract the information from particular types of scientific figure, to create comprehensive "super" figures: a giant network of all the known chemical processes in a cell for example, or the best-available tree of life. At just one such superfigure per paper, though, the citation records of articles containing such all-embracing diagrams may very well undermine the correlation that prompted their creation in the first place. Call it the ultimate marriage of chart and science.

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Content

Vgl. unter: http://eprints.rclis.org/28725/. Der Artikel basiert auf meiner mit dem VFI-Förderungspreis 2014 prämierten Masterarbeit "Informationsvisualisierung und Retrieval im Fokus der Informationspraxis". Diese ist auf dem Publikationsserver des Instituts für Informationswissenschaft der Fachhochschule Köln unter "http://publiscologne.fh-koeln.de/frontdoor/index/index/id/334/docId/334" verfügbar.

Source

Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 68(2015) H.3/4, S.416-438

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Abstract

The changes in KO systems induced by sociocultural influences may include those in both classificatory principles and cultural features. The proposed study will examine the Korean Decimal Classification (KDC)'s adaptation of the Dewey Decimal Classification (DDC) by comparing the two systems. This case manifests the sociocultural influences on KOSs in a cross-cultural context. Therefore, the study aims at an in-depth investigation of sociocultural influences by situating a KOS in a cross-cultural environment and examining the dynamics between two classification systems designed to organize information resources in two distinct sociocultural contexts. As a preceding stage of the comparison, the analysis was conducted on the changes that result from the meeting of different sociocultural feature in a descriptive method. The analysis aims to identify variations between the two schemes in comparison of the knowledge structures of the two classifications, in terms of the quantity of class numbers that represent concepts and their relationships in each of the individual main classes. The most effective analytic strategy to show the patterns of the comparison was visualizations of similarities and differences between the two systems. Increasing or decreasing tendencies in the class through various editions were analyzed. Comparing the compositions of the main classes and distributions of concepts in the KDC and DDC discloses the differences in their knowledge structures empirically. This phase of quantitative analysis and visualizing techniques generates empirical evidence leading to interpretation.

Content

Beitrag bei: NASKO 2017: Visualizing Knowledge Organization: Bringing Focus to Abstract Realities. The sixth North American Symposium on Knowledge Organization (NASKO 2017), June 15-16, 2017, in Champaign, IL, USA.

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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.

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Abstract

The editor's decision where and how to place items on a screen is crucial for the design of information displays, such as websites. We developed a statistical model that can facilitate automating this process by predicting the perceived importance of screen items from their location and size. The model was developed based on a 2-step experiment in which we asked participants to rate the importance of text articles that differed in size, screen location, and title size. Articles were either presented for 0.5 seconds or for unlimited time. In a stepwise regression analysis, the model's variables accounted for 65% of the variance in the importance ratings. In a validation study, the model predicted 85% of the variance of the mean apparent importance of screen items. The model also predicted individual raters' importance perception ratings. We discuss the implications of such a model in the context of automating layout generation. An automated system for layout generation can optimize data presentation to suit users' individual information and display preferences.

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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.

Content

Beitrag bei: NASKO 2017: Visualizing Knowledge Organization: Bringing Focus to Abstract Realities. The sixth North American Symposium on Knowledge Organization (NASKO 2017), June 15-16, 2017, in Champaign, IL, USA.

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Abstract

Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.

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Series

Advances in knowledge organization; vol.16

Source

Challenges and opportunities for knowledge organization in the digital age: proceedings of the Fifteenth International ISKO Conference, 9-11 July 2018, Porto, Portugal / organized by: International Society for Knowledge Organization (ISKO), ISKO Spain and Portugal Chapter, University of Porto - Faculty of Arts and Humanities, Research Centre in Communication, Information and Digital Culture (CIC.digital) - Porto. Eds.: F. Ribeiro u. M.E. Cerveira

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Abstract

The present study reports on the information seeking processes in a visual context, referred to throughout as visual information seeking. This study synthesizes research throughout different, yet complementary, areas, each capable of contributing findings and understanding to visual information seeking. Methods previously applied for examining the visual information seeking process are reviewed, including interactive experiments, surveys, and various qualitative approaches. The methods and resulting findings are presented and structured according to generalized phases of existing information seeking models, which include the needs, actions, and assessments of users. A review of visual information needs focuses on need and thus query formulation; user actions, as reviewed, centers on search and browse behaviors and the observed trends, concluded by a survey of users' assessments of visual information as part of the interactive process. This separate examination, specific to a visual context, is significant; visual information can influence outcomes in an interactive process and presents variations in the types of needs, tasks, considerations, and decisions of users, as compared to information seeking in other contexts.

Series

Advances in information science

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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.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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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.

Content

Paper presented at: IFLA WLIC 2015 - Cape Town, South Africa in Session 141 - Science and Technology.

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Abstract

In the 1950s, the "universe of knowledge" metaphor returned in discussions around the "first theory of faceted classification'; the Colon Classification (CC) of S.R. Ranganathan, to stress the differences within an "universe of concepts" system. Here we claim that the Universal Decimal Classification (UDC) has been either ignored or incorrectly represented in studies that focused on the pivotal role of Ranganathan in a transition from "top-down universe of concepts systems" to "bottom-up universe of concepts systems." Early 20th century designs from Paul Otlet reveal a two directional interaction between "elements" and "ensembles"that can be compared to the relations between the universe of knowledge and universe of concepts systems. Moreover, an unpublished manuscript with the title "Theorie schematique de la Classification" of 1908 includes sketches that demonstrate an exploration by Paul Otlet of the multidimensional characteristics of the UDC. The interactions between these one- and multidimensional representations of the UDC support Donker Duyvis' critical comments to Ranganathan who had dismissed it as a rigid hierarchical system in comparison to his own Colon Classification. A visualization of the experiments of the Knowledge Space Lab in which main categories of Wikipedia were mapped on the UDC provides empirical evidence of its faceted structure's flexibility.

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Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.

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Abstract

Purpose - The purpose of this paper is to evaluate the functionality of the particular epistemological schools with regard to the issues of users with visual impairment, to offer a theoretical answer to the question why these issues are not in the center of the interest of information science, and to try to find an epistemological approach that has ambitions to create the theoretical basis for the analysis of the relationship between information and visually impaired users. Design/methodology/approach - The methodological basis of the paper is determined by the selection of the epistemological approach. In order to think about the concept of information and to put it in relation to issues associated with users with visual impairment, a conceptual analysis is applied. Findings - Most of information science theories are based on empiricism and rationalism; this is the reason for their low interest in the questions of visually impaired users. Users with visual disabilities are out of the interest of rationalistic epistemology because it underestimates sensory perception; empiricism is not interested in them paradoxically because it overestimates sensory perception. Realism which fairly reflects such issues is an approach which allows the providing of information to persons with visual disabilities to be dealt with properly. Research limitations/implications - The paper has a speculative character. Its findings should be supported by empirical research in the future. Practical implications - Theoretical questions solved in the paper come from the practice of providing information to visually impaired users. Because practice has an influence on theory and vice versa, the author hopes that the findings included in the paper can serve to improve practice in the field. Social implications - The paper provides theoretical anchoring of the issues which are related to the inclusion of people with disabilities into society and its findings have a potential to support such efforts. Originality/value - This is first study linking questions of users with visual disabilities to highly abstract issues connected to the concept of information.

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Abstract

Purpose - The authors introduce two semantics-based navigation applications that facilitate information-seeking activities in internal link-based web sites in Wikipedia. These applications aim to help users find concepts within a topic and related articles on a given topic quickly and then gain topical knowledge from internal link-based encyclopedia web sites. The paper aims to discuss these issues. Design/methodology/approach - The WNavis application consists of three information visualization (IV) tools which are a topic network, a hierarchy topic tree and summaries for topics. The WikiMap application consists of a topic network. The goal of the topic network and topic tree tools is to help users to find the major concepts of a topic and identify relationships between these major concepts easily. In addition, in order to locate specific information and enable users to explore and read topic-related articles quickly, the topic tree and summaries for topics tools support users to gain topical knowledge quickly. The authors then apply the k-clique of cohesive indicator to analyze the sub topics of the seed query and find out the best clustering results via the cosine measure. The authors utilize four metrics, which are correctness, time cost, usage behaviors, and satisfaction, to evaluate the three interfaces. These metrics measure both the outputs and outcomes of applications. As a baseline system for evaluation the authors used a traditional Wikipedia interface. For the evaluation, the authors used an experimental user study with 30 participants.
Findings - The results indicate that both WikiMap and WNavis supported users to identify concepts and their relations better compared to the baseline. In topical tasks WNavis over performed both WikiMap and the baseline system. Although there were no time differences in finding concepts or answering topical questions, the test systems provided users with a greater gain per time unit. The users of WNavis leaned on the hierarchy tree instead of other tools, whereas WikiMap users used the topic map. Research limitations/implications - The findings have implications for the design of IR support tools in knowledge-intensive web sites that help users to explore topics and concepts. Originality/value - The authors explored to what extent the use of each IV support tool contributed to successful exploration of topics in search tasks. The authors propose extended task-based evaluation measures to understand how each application provides useful context for users to accomplish the tasks and attain the search goals. That is, the authors not only evaluate the output of the search results, e.g. the number of relevant items retrieved, but also the outcome provided by the system for assisting users to attain the search goal.

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Abstract

The use of research impact metrics and analytics has become an integral component to many aspects of institutional assessment. Many platforms currently exist to provide such analytics, both proprietary and open source; however, the functionality of these systems may not always overlap to serve uniquely specific needs. In this paper, I describe a novel web-based platform, named Manifold, that I built to serve custom research impact assessment needs in the University of Minnesota Medical School. Built on a standard LAMP architecture, Manifold automatically pulls publication data for faculty from Scopus through APIs, calculates impact metrics through automated analytics, and dynamically generates report-like profiles that visualize those metrics. Work on this project has resulted in many lessons learned about challenges to sustainability and scalability in developing a system of such magnitude.

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Abstract

This article shows how visual representations have progressively taken the lead over classical language-based models of knowledge organization (KO). The paper adopts a theoretical and historical perspective and focuses on the consequences of the changes in the volume of data generated by data production on the KO models. Until now, data visualization tools have been used mainly by researchers with expertise in textual data processing or in computational linguistics. But now, these tools are accessible to a greater number of users. Thus, there are new issues at stake for KO, other professions and institutions for gathering data that contribute to defining new standards and KO representations.

Footnote

Beitrag in einem Special Issue: Best papers from NASKO, ISKO-UK, ISKO-France, ISKO-Brazil 2019.

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Abstract

This paper proposes a novel methodology to visualize a classification scheme. It is demonstrated with the Association for Computing Machinery (ACM) Computing Classification System (CCS). The collection derived from the ACM digital library, containing 37,543 documents classified by CCS. The assigned classes, subject descriptors, and keywords were processed in a dataset to produce a graphical representation of the documents. The general conception is based on the similarity of co-classes (themes) proportional to the number of common publications. The final number of all possible classes and subclasses in the collection was 353 and therefore the similarity matrix of co-classes had the same dimension. A spherical surface was chosen as the target information space. Classes and documents' node locations on the sphere were obtained by means of Multidimensional Scaling coordinates. By representing the surface on a plane like a map projection, it is possible to analyze the visualization layout. The graphical patterns were organized in some colour clusters. For evaluation of given visualization maps, graphics filtering was applied. This proposed method can be very useful in interdisciplinary research fields. It allows for a great amount of heterogeneous information to be conveyed in a compact display, including topics, relationships among topics, frequency of occurrence, importance and changes of these properties over time.

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Abstract

We sought to understand how users interpret meanings of symbols commonly used in information systems, especially how icons are processed by the brain. We investigated Chinese and English speakers' processing of 4 types of visual stimuli: icons, pictures, Chinese characters, and English words. The goal was to examine, via functional magnetic resonance imaging (fMRI) data, the hypothesis that people cognitively process icons as logographic words and to provide neurological evidence related to human-computer interaction (HCI), which has been rare in traditional information system studies. According to the neuroimaging data of 19 participants, we conclude that icons are not cognitively processed as logographical words like Chinese characters, although they both stimulate the semantic system in the brain that is needed for language processing. Instead, more similar to images and pictures, icons are not as efficient as words in conveying meanings, and brains (people) make more effort to process icons than words. We use this study to demonstrate that it is practicable to test information system constructs such as elements of graphical user interfaces (GUIs) with neuroscience data and that, with such data, we can better understand individual or group differences related to system usage and user-computer interactions.