KC-Viz: a novel approach to visualizing and nNavigating ontologies

There is empirical evidence that the user interaction metaphors used in ontology engineering toolkits are largely inadequate and that novel interactive frameworks for human ontology interaction are needed. Here we present a novel tool for visualizing and navigating ontologies, called KC Viz, which exploits an innovative ontology summarization method to support a ’middleout ontology browsing’ approach, where it becomes possible to navigate ontologies starting from the most information-rich nodes (i.e., key concepts). This approach is similar to map-based visualization and navigation in Geographical Information Systems, where, e.g., major cities are displayed more prominently than others, depending on the current level of granularity

Multi-View Ontology Explorer (MOE): Interactive Visual Exploration of Ontologies

An ontology is an explicit specification of a conceptualization. This specification consists of a common vocabulary and information structure of a domain. Ontologies have applications in many fields to semantically link information in a standardized manner. In these fields, it is often crucial for both expert and non-expert users to quickly grasp the contents of an ontology; and to achieve this, many ontology tools implement visualization components. There are many past works on ontology visualization, and most of these tools are adapted from tree and graph based visualization techniques (e.g. treemaps, node-link graphs, and 3D interfaces). However, due to the enormous size of ontologies, these existing tools have their own shortcomings when dealing information overload, usually resulting in clutter and occlusion on the screen. In this thesis, we propose a set of novel visualizations and interactions to visualize very large ontologies. We design 5 dynamically linked visualizations that focus on a different level of abstraction individually. These different levels of abstraction start from a high-level overview down to a low-level entity. In addition, these visualizations collectively visualize landmarks, routes, and survey knowledge to support the formation of mental models. Search and save features are implemented to support on-demand and guided exploration. Finally, we implement our design as a web application

Visualizing consensus with online ontologies to support quality in ontology development

Representing a consensus is one of the most important of the qualities of an ontology. Generally, consensus is understood in ontology engineering as an accurate representation of a shared view or model within an ontology. However, we can also show that, while developing an ontology, checking for the agreement of other existing ontologies that have been published online can be very valuable. Indeed, relying on the Watson Semantic Web search engine and on existing measures of agreement, consensus and controversy in ontologies, we develop a visualization method that allows to emphasize `areas' of an ontology which are in disagreement with online ontologies, as well as elements touching on controversial statements, for which a clear consensus has not been established. Integrating this visualization into the ontology development environment allows for the ontology engineer to quickly identify elements of the ontology which quality might need to be improved, as they relate to such generally disagreed or controversial areas

Comparison and evaluation of ontology visualizations

In the last couple of years a large number of software aiming at visualizing ontologies were introduced, causing difficulties in selecting an objectively suitable visualizing tool. This work lists free and commercial tools in four groups named by the visualizing method used by members of each group. Furthermore a detailed examination of each tool in a separate section is presented. Finally the document comes to an end with an overview of all listed tools and a recommendation of the most powerful ones of each group

Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

Augmenting the Ontology Visualization Tool Recommender: Input Pre-Filling and Integration with the OOSP Ontological Benchmark Builder

ABSTRACT Ontology visualization is an important functionality for ontology users. Since many visualization methods have been proposed and implemented, it is not an easy task for an ontology user to select a proper ontology visualization tool based on the user's requirements. In order to recommend an ontology visualization tool, some requirements should be provided manually by the user. However, other requirements could be assessed automatically. This demo paper presents a partial automation of the pre-existing Ontology Visualization Tools Recommender input; the recommender knowledge base is also updated with three new ontology visualization tools. Further, we present an integration of the Ontology Visualization Tools Recommender into another web-based tool, Online Ontology Set Picker, where proper ontology visualization can assist in ontology benchmark construction

Semantic Publishing: issues, solutions and new trends in scholarly publishing within the Semantic Web era

This work is concerned with the increasing relationships between two distinct multidisciplinary research fields, Semantic Web technologies and scholarly publishing, that in this context converge into one precise research topic: Semantic Publishing. In the spirit of the original aim of Semantic Publishing, i.e. the improvement of scientific communication by means of semantic technologies, this thesis proposes theories, formalisms and applications for opening up semantic publishing to an effective interaction between scholarly documents (e.g., journal articles) and their related semantic and formal descriptions. In fact, the main aim of this work is to increase the users' comprehension of documents and to allow document enrichment, discovery and linkage to document-related resources and contexts, such as other articles and raw scientific data. In order to achieve these goals, this thesis investigates and proposes solutions for three of the main issues that semantic publishing promises to address, namely: the need of tools for linking document text to a formal representation of its meaning, the lack of complete metadata schemas for describing documents according to the publishing vocabulary, and absence of effective user interfaces for easily acting on semantic publishing models and theories