Implicit information embedded in semantic web graphs, such as topography, clusters, and disconnected subgraphs is difficult to extract from text files. Visualizations of the graphs can reveal some of these features, but existing systems for visualizing metadata focus on aspects other than understanding the greater structure. In this paper, we present a tool for generating visualizations of ontologies and metadata by using a modified spring embedder to achieve an automatic layout. Through a case study using a mid-sized ontology, we show that interesting information about the data relationships can be extracted through our visualization of the physical graph structure.

Internet Relay Chat is a system that allows groups of people to collaborate and chat from anywhere in the world. Clearly defined by several RFC documents, it is arguably the most standard real-time chat system currently in use. This paper describes a method of inferring the social network of a group of IRC users in a channel. An IRC bot is used to monitor a channel and perform a heuristic analysis of events to create a mathematical approximation of the social network. From this, the bot can produce a visualization of the inferred social network on demand. These visualizations reveal the structure of the social network, highlighting connectivity, clustering and strengths of relationships between users. Animated output allows viewers to see the evolution of the social network over time. Some novel ideas for future work are discussed, showing other useful applications of this system.

Abstract. In many practical applications, ontologies tend to be very large and complicated. In order for users to quickly understand and analyze large-scale ontologies, in this paper we propose a novel ontology visualization approach, which aims to complement existing approaches like the hierarchy graph. Specifically, our approach produces a holistic “imaging ” of the ontology which contains a semantic layout of the ontology classes. In addition, the distributions of the ontology instances and instance relations are also depicted in the “imaging”. We introduce at length the key techniques and algorithms used in our approach. Then we examine the resulting user interface and find it facilitates tasks like ontology navigation, ontology retrieval and ontology instance analysis. 1

Geovisualizers often need to represent data that consists of items related together. Such data sets can be abstracted to a mathematical structure, the graph. A graph contains nodes and edges where the nodes represent the items or concepts of interest, and the edges connect two nodes together according to some associational scheme. Examples of graph data include: network topologies; maps, where nodes represent

Ontology evolution is one of the key problems facing ontology users today. Adapting ontologies to meet new requirements involves understanding various sections of ontologies and the changes made thereafter. Currently, a large amount of research has been undertaken in automatic change detection and change effects, however the process is far from automated. Users are involved at almost every step and with ontologies becoming more commonplace and complex, tools are required to lighten the large cognitive load. Initially, we present the requirements and needs for a visualisation solution in order to streamline ontology evolution, followed by the research into a graph based prototype, integrated with Protégé. 2. Summary of Visualisation Requirements The following list of requirements are applicable to visualising changes to ontologies. They have been derived from papers and interviews with users. In addition, we have approached the problem from a second angle; that presented by Gary Ng [6] which is discussed later. 1. Representation of the data transformed between two ontology versions[3]. 2. Informing the user of Axiom validity across changes[3]. 3. Distinguish Semantic and Syntactical changes[2][9] 4. Highlight potential specification violations resulting from semantic changes[2].

Abstract—Biologists hope to address grand scientific challenges by exploring the abundance of data made available through microarray analysis and other high-throughput techniques. However, the impact of this large volume of data is limited unless researchers can effectively assimilate the entirety of this complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression can make use of novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters and achieving data reduction. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression. Index Terms—Visualization in physical sciences, life sciences and engineering, graph and network visualization, bioinformatics visualization, focus+context techniques 1

Implicit information embedded in Semantic Web graphs, such as topography, clusters, and disconnected subgraphs, is difficult to extract from text files. Visualizations of the graphs can reveal some of these features, but existing systems for visualizing metadata focus on aspects other than understanding the greater structure. In this