Word clouds have emerged as a straightforward and visually appealing visualization method for text. They are used in various contexts as a means to provide an overview by distilling text down to those words that appear with highest frequency. Typically, this is done in a static way as pure text summarization. We think, however, that there is a larger potential to this simple yet powerful visualization paradigm in text analytics. In this work, we explore the usefulness of word clouds for general text analysis tasks. We developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method. It equips them with advanced natural language processing, sophisticated interaction techniques, and context information. We show how this approach can be effectively used to solve text analysis tasks and evaluate it in a qualitative user study.

The Visual Notation for OWL Ontologies (VOWL) is a well-specified visual language for the user-oriented representation of ontologies. It defines graphical depictions for most elements of the Web Ontology Language (OWL) that are combined to a force-directed graph layout visualizing the ontology. In contrast to related work, VOWL aims for an intuitive and comprehensive representation that is also understandable to users less familiar with ontologies. This article presents VOWL in detail and describes its implementation in two different tools: ProtégéVOWL and WebVOWL. The first is a plugin for the ontology editor Protégé, the second a standalone web application. Both tools demonstrate the applicability of VOWL by means of various ontologies. In addition, the results of three user studies that evaluate the comprehensibility and usability of VOWL are summarized. They are complemented by findings from an interview with experienced ontology users and from testing the visual scope and completeness of VOWL with a benchmark ontology. The evaluations helped to improve VOWL and confirm that it produces comparatively intuitive and comprehensible ontology visualizations.

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Abstract. Tag clouds have become a popular visualization and navigation interface on the Web. Despite their popularity, little is known about tag cloud perception and performance with respect to different user goals. This paper presents results from a comparative study of several tag cloud layouts. The results show differences in task performance, leading to the conclusion that interface designers should carefully select the appropriate tag cloud layout according to the expected user goals. Furthermore, the analysis of eye tracking data provides insights into the visual exploration strategies of tag cloud users.

Various ontology visualization tools using different visualization methods exist and new ones are being developed every year. The goal of this paper is to follow up on previous surveys with an updated classification of ontology visualization methods and a comprehensive survey of available tools. The tools are analyzed for the used visualization methods, interaction techniques and supported ontology constructs. It shows that most of the tools apply two-dimensional node-link visualizations with a focus on class hierarchies. Color and shape are used with little variation, support for constructs introduced with version 2 of the OWL Web Ontology Language is limited, and it often remains vague what tasks and use cases are supported by the visualizations. Major challenges are the limited maturity and usability of many of the tools as well as providing an overview of large ontologies while also showing details on demand. We see a high demand for a universal ontology visualization framework implementing a core set of visual and interactive features that can be extended and customized to respective use cases.

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This paper presents an approach for the interactive discovery of relationships between selected elements via the Semantic Web. It emphasizes the human aspect of relationship discovery by offering sophisticated interaction support. Selected elements are first semi-automatically mapped to unique objects of Semantic Web datasets. These datasets are then crawled for relationships which are presented in detail and overview. Interactive features and visual clues allow for a sophisticated exploration of the found relationships. The general process is described and the RelFinder tool as a concrete implementation and proof-of-concept is presented and evaluated in a user study. The application potentials are illustrated by a scenario that uses the RelFinder and DBpedia to assist a business analyst in decision-making. Main contributions compared to previous and related work are data aggregations on several dimensions, a graph visualization that displays and connects relationships also between more than two given objects, and an advanced implementation that is highly configurable and applicable to arbitrary RDF datasets.

This paper brings the aid effectiveness debate to the sub-national level. We hypothesize the nonrobust results regarding the effects of aid on development in the previous literature to arise due to the effects of aid being insufficiently large to measurably affect aggregate outcomes. Using geocoded data for World Bank aid to a maximum of 2,221 first-level administrative regions (ADM1) and 54,167 second-level administrative regions (ADM2) in 130 countries over the 2000-2011 period, we test whether aid affects development, measured as nighttime light growth. Our preferred identification strategy exploits variation arising from interacting a variable that indicates whether or not a country has passed the threshold for receiving IDA's concessional aid with a recipient region's probability to receive aid, in a sample of 478 ADM1 regions and almost 8,400 ADM2 regions from 21 countries. Controlling for the levels of the interacted variables, the interaction provides a powerful and excludable instrument. Overall, we find significant correlations between aid and growth in ADM2 regions, but no causal effects.