Visual knowledge representation of conceptual semantic networks

This article presents methods of using visual analysis to visually represent large amounts of massive, dynamic, ambiguous data allocated in a repository of learning objects. These methods are based on the semantic representation of these resources. We use a graphical model represented as a semantic graph. The formalization of the semantic graph has been intuitively built to solve a real problem which is browsing and searching for lectures in a vast repository of colleges/courses located at Western Kentucky University1. This study combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia2 platform. Also, we argue that the most important factor in building the semantic representation is defining the hierarchical structure and the relationships among concepts and subconcepts. In addition, we investigate the association between concepts using Concept Analysis to generate a lattice graph. Our domain is considered as a graph, which represents the integrated ontology of the HyperManyMedia platform. This approach has been implemented and used by online students at WKU3

HEALTH GeoJunction: place-time-concept browsing of health publications

<p>Abstract</p> <p>Background</p> <p>The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces <it><smcaps>HEALTH</smcaps> GeoJunction</it>, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically.</p> <p>Results</p> <p><it><smcaps>HEALTH</smcaps> GeoJunction </it>is a geovisual analytics-enabled web application providing: (a) web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b) visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized.</p> <p>Conclusions</p> <p>PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to rapidly escalating attention to geographic factors in public health. The implementation of mechanisms for iterative place-time-concept filtering makes it possible to narrow searches efficiently and quickly from thousands of documents to a small subset that meet place-time-concept constraints. Support for a <it>more-like-this </it>query creates the potential to identify unexpected connections across diverse areas of research. Multi-view visualization methods support understanding of the place, time, and concept components of document collections and enable comparison of filtered query results to the full set of publications.</p

Untangling the Web of Historical Thinking: What the Structures of Student-Produced Wikis Reveal

During the past decade, most educators have focused on the collaborative aspects of wikis and how they enable us to collectively create web content. Our focus has been different.We are interested in the ways using wikis to structure information helps students learn the complex thinking skills necessary for understanding history as a web of knowledge rather than linear. We are also interested in the information about how and when students cross concept thresholds made visible by the interconnections of the different pages in a wiki. Using Luminotes, a rudimentary, yet elegant open-source wiki, students can represent--and, we theorize, better understand--the ways that knowledge in the discipline of history is constructed. By the end of the grant period we hope to demonstrate that wikis can be broadly used as a powerful tool for structuring the rich, relational character of knowledge in history (and, potentially, other humanities disciplines) and for better understanding student learning

Evaluating tag-based information access in image collections

The availability of social tags has greatly enhanced access to information. Tag clouds have emerged as a new "social" way to find and visualize information, providing both one-click access to information and a snapshot of the "aboutness" of a tagged collection. A range of research projects explored and compared different tag artifacts for information access ranging from regular tag clouds to tag hierarchies. At the same time, there is a lack of user studies that compare the effectiveness of different types of tag-based browsing interfaces from the users point of view. This paper contributes to the research on tag-based information access by presenting a controlled user study that compared three types of tag-based interfaces on two recognized types of search tasks - lookup and exploratory search. Our results demonstrate that tag-based browsing interfaces significantly outperform traditional search interfaces in both performance and user satisfaction. At the same time, the differences between the two types of tag-based browsing interfaces explored in our study are not as clear. Copyright 2012 ACM

SUPPORT EFFECTIVE DISCOVERY MANAGEMENT IN VISUAL ANALYTICS

Visual analytics promises to supply analysts with the means necessary to ana- lyze complex datasets and make effective decisions in a timely manner. Although significant progress has been made towards effective data exploration in existing vi- sual analytics systems, few of them provide systematic solutions for managing the vast amounts of discoveries generated in data exploration processes. Analysts have to use off line tools to manually annotate, browse, retrieve, organize, and connect their discoveries. In addition, they have no convenient access to the important discoveries captured by collaborators. As a consequence, the lack of effective discovery manage- ment approaches severely hinders the analysts from utilizing the discoveries to make effective decisions. In response to this challenge, this dissertation aims to support effective discov- ery management in visual analytics. It contributes a general discovery manage- ment framework which achieves its effectiveness surrounding the concept of patterns, namely the results of users’ low-level analytic tasks. Patterns permit construction of discoveries together with users’ mental models and evaluation. Different from the mental models, the categories of patterns that can be discovered from data are pre- dictable and application-independent. In addition, the same set of information is often used to annotate patterns in the same category. Therefore, visual analytics sys- tems can semi-automatically annotate patterns in a formalized format by predicting what should be recorded for patterns in popular categories. Using the formalized an- notations, the framework also enhances the automation and efficiency of a variety of discovery management activities such as discovery browsing, retrieval, organization, association, and sharing. The framework seamlessly integrates them with the visual interactive explorations to support effective decision making. Guided by the discovery management framework, our second contribution lies in proposing a variety of novel discovery management techniques for facilitating the discovery management activities. The proposed techniques and framework are im- plemented in a prototype system, ManyInsights, to facilitate discovery management in multidimensional data exploration. To evaluate the prototype system, two long- term case studies are presented. They investigated how the discovery management techniques worked together to benefit exploratory data analysis and collaborative analysis. The studies allowed us to understand the advantages, the limitations, and design implications of ManyInsights and its underlying framework

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.Comment: 45 pages, 14 figures, 4 table

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..