23,806 research outputs found
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
Co-authorship networks in Swiss political research
Co-authorship is an important indicator of scientific collaboration. Co-authorship networks are composed of sub-communities, and researchers can gain visibility by connecting these insulated subgroups. This article presents a comprehensive co-authorship network analysis of Swiss political science. Three levels are addressed: disciplinary cohesion and structure at large, communities, and the integrative capacity of individual researchers. The results suggest that collaboration exists across geographical and language borders even though different regions focus on complementary publication strategies. The subfield of public policy and administration has the highest integrative capacity. Co-authorship is a function of several factors, most importantly being in the same subfield. At the individual level, the analysis identifies researchers who belong to the “inner circle” of Swiss political science and who link different communities. In contrast to previous research, the analysis is based on the full set of publications of all political researchers employed in Switzerland in 2013, including past publications
Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
Information visualization: conceptualizing new paths for filtering and navigate in scientific knowledge objects
More than 6,849.32 new research journal articles are published every day! Who has time to read every article or document that’s relevant to their research? Access to the right and relevant information is paramount for scientific discoveries. Filtering relevant information has become a fundamental challenge in the actual scientific deluge panorama. As information glut grows ever worse, understanding and visualizing the science social behavior may become our only hope for handling a growing deluge of scientific information. It is therefore fundamental to analyze and interactively visualize the science social space. This paper theoretically conceptualizes an approach aimed at the filtering and navigation of relevant Scientific Knowledge Objects (SKOs) based on a symbiosis between different sub-disciplines domains. We present two main contributions, a comparison among several projects with some relevant use of information visualization in scholarly scientific navigation; and an architecture which will be in line with the most recent international standards and good practices for Open Data, especially those related to Linked Open Data capable to perform an innovative information visualization of relevant SKOs. These contributions are relevant to scholarly and to practitioner’s communities and to who want to access and navigate in relevant SKOs.This work has been supported by COMPETE: POCI-01-
0145-FEDER-007043 and FCT – Fundação para a Ciência e
Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Citation chain aggregation: An interaction model to support citation cycling
This is the postprint version of the conference paper.Citation chaining is a powerful means of exploring the academic literature. Starting from just one or two known relevant items, a
naïve researcher can cycle backwards and forwards through the citation graph to generate a rich overview of key works, authors and journals relating to their topic. Whilst online citation indexes
greatly facilitate this process, the size and complexity of the search space can rapidly escalate. In this paper, we propose a
novel interaction model called citation chain aggregation (CCA). CCA employs a simple three-list view which highlights the
overlaps that occur between the first-generation relations of known relevant items. As more relevant articles are identified, differences in the frequencies of citations made by or to unseen articles provide strong relevance feedback cues. The benefits of this technique are illustrated using a simple case study
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