RDF Knowledge Graph Visualization From a Knowledge Extraction System

In this paper, we present a system to visualize RDF knowledge graphs. These graphs are obtained from a knowledge extraction system designed by GEOLSemantics. This extraction is performed using natural language processing and trigger detection. The user can visualize subgraphs by selecting some ontology features like concepts or individuals. The system is also multilingual, with the use of the annotated ontology in English, French, Arabic and Chinese

Fostering collaborative knowledge construction with visualization tools

This study investigates to what extent collaborative knowledge construction can be fostered by providing students with visualization tools as structural support. Thirty-two students of Educational Psychology took part in the study. The students were subdivided into dyads and asked to solve a case problem of their learning domain under one of two conditions: 1) with content-specific visualization 2) with content-unspecific visualization. Results show that by being provided with a content-specific visualization tool, both the process and the outcome of the cooperative effort improved. More specifically, dyads under that condition referred to more adequate concepts, risked more conflicts, and were more successful in integrating prior knowledge into the collaborative solution. Moreover, those learning partners had a more similar individual learning outcome

Educational Technology as Seen Through the Eyes of the Readers

In this paper, I present the evaluation of a novel knowledge domain visualization of educational technology. The interactive visualization is based on readership patterns in the online reference management system Mendeley. It comprises of 13 topic areas, spanning psychological, pedagogical, and methodological foundations, learning methods and technologies, and social and technological developments. The visualization was evaluated with (1) a qualitative comparison to knowledge domain visualizations based on citations, and (2) expert interviews. The results show that the co-readership visualization is a recent representation of pedagogical and psychological research in educational technology. Furthermore, the co-readership analysis covers more areas than comparable visualizations based on co-citation patterns. Areas related to computer science, however, are missing from the co-readership visualization and more research is needed to explore the interpretations of size and placement of research areas on the map.Comment: Forthcoming article in the International Journal of Technology Enhanced Learnin

Adaptive visualization of research communities

Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects

Understanding Visualization: A formal approach using category theory and semiotics

This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not

Beyond information extraction: The role of ontology in military report processing

Information extraction tools like SMES transform natural language into formal representation, e.g. into feature structures. Doing so, these tools exploit and apply linguistic knowledge about the syntactic and morphological regularities of the language used. However, these tools apply semantic as well as pragmatic knowledge only partially at best. Automatic processing of military reports has to result in a visualization of the reports content by map as well as in an actualization of the underlying database in order to allow for the actualization of the common operational picture. Normally, however, the information provided by the result of the information extraction is not explicit enough for visualization processes and database insertions. This originates from the reports themselves that are elliptical, ambiguous, and vague. In order to overcome this obstacle, the situational context and thus semantic and pragmatic aspects have to be taken into account. In the paper at hand, we present a system that uses an ontological module to integrate semantic and pragmatic knowledge. The result of the completion contains all the specifications to allow for a visualization of the report’s content on a map as well as for a database actualization

Knowledge visualization by Doctus Knowledge Galaxy

Making an attempt to develop a curriculum for web-based learning, we have realized that the existing solutions have adopted the traditional content management principles of printed books. By doing so, these solutions do not make use of benefits of a web-based system, namely the multimedia and the interactivity. Our new solution puts these benefits into the focus. Combining the features of semantic networks, cognitive maps and machine learning, we have developed a new generation knowledge visualization tool called Doctus Knowledge Galaxy shell. In our solution the topics and keywords are not in a sequential order, thus enabling the e-learner to choose her/his own learning route. The monitoring system of Doctus Knowledge Galaxy also allow us to observe how much time the learner spends on particular keywords and which next keyword she/he chooses after concluding a previous one by passing the test. The most important achievement of Doctus Knowledge Galaxy is its clear and transparent structure, which enables the learners into fast navigation and provides the developers with useful information about the learning routes and performance of the learner