Touch-Based Ontology Browsing on Tablets and Surfaces

Semantic technologies and Linked Data are increasingly adopted as core application modules, in many knowledge domains and involving various stakeholders: ontology engineers, software architects, doctors, employees, etc. Such a diffusion calls for better access to models and data, which should be direct, mobile, visual and time effective. While a relevant core of research efforts investigated the problem of ontology visualization, discovering different paradigms, layouts, and interaction modalities, a few approaches target mobile devices such as tablets and smartphones. Touch interaction, indeed, has the potential of dramatically improving usability of Linked Data and of semantic-based solutions in real-world applications and mash-ups, by enabling direct and tactile interactions with involved knowledge objects. In this paper, we move a step towards touch-based, mobile interfaces for semantic models by presenting an ontology browsing platform for Android devices. We exploit state of the art touch-based interaction paradigms, e.g., pie menus, pinch-to-zoom, etc., to empower effective ontology browsing. Our research mainly focuses on interactions, yet providing support to different visualization approaches thanks to a clear decoupling between model-level operation and visual representations. Presented results include the design and implementation of a working prototype application, as well as a first validation involving habitual users of semantic technologies. Results show a low learning curve and positive reactions to the proposed paradigms, which are perceived as both innovative and useful

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

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

RDF Graph Visualization Tools: a Survey

Semantic Web technologies are increasingly being used for the development of Future Internet applications, mainly due to the impressive growth of the Internet of Things research area. This spread pushes for effective and efficient ways to visualize the content of RDF ontologies and knowledge bases. Several strategies can be adopted to visualize semantic data and one of this consists in exploiting the graph representation intrinsic in the RDF model. In this paper, we propose a survey of the main tools for the graphical visualization of triples (being them terminological or assertional statements) exploiting a graph representation

VOWL 2: User-Oriented Visualization of Ontologies

Abstract. Ontologies become increasingly important as a means to structure and organize information. This requires methods and tools that enable not only ontol-ogy experts but also other user groups to work with ontologies and related data. We have developed VOWL, a comprehensive and well-specified visual language for the user-oriented representation of ontologies, and conducted a comparative study on an initial version of VOWL. Based upon results from that study, as well as an extensive review of other ontology visualizations, we have reworked many parts of VOWL. In this paper, we present the new version VOWL 2 and describe how the initial definitions were used to systematically redefine the visual notation. Besides the novelties of the visual language, which is based on a well-defined set of graphical primitives and an abstract color scheme, we briefly describe two im-plementations of VOWL 2. To gather some insight into the user experience with the new version of VOWL, we have conducted a qualitative user study. We report on the study and its results, which confirmed that not only the general ideas of VOWL but also most of our enhancements for VOWL 2 can be well understood by casual ontology users

Ontologies for Knowledge modeling in construction planning

Nowadays, there is an increasing recognition of the value of knowledge management in the construction projects and ontology-based semantic modelling is seen as an important means of addressing this problem, even if a knowledge-base which maps the construction planning and scheduling domains, in a formal and machine-readable way, is still missing. Addressing this issue, the book is divided in two parts. Part I, theory, is a theoretical introduction of on ontologies concepts and expert systems. Part II, application, presents a research of ontologies development for semantic modelling of construction scheduling, workspace, product and time domains. The last chapter presents the architecture of an ontology-based expert system, to show how ontologies can support automated planning mechanisms

Semantic Web and the Web of Things: concept, platform and applications

The ubiquitous presence of devices with computational resources and connectivity is fostering the diffusion of the Internet of Things (IoT), where smart objects interoperate and react to the available information providing services to the users. The pervasiveness of the IoT across many different areas proves the worldwide interest of researchers from academic and enterprises worlds. This Research has brought to new technologies and protocols addressing different needs of emerging scenarios, making difficult to develop interoperable applications. The Web of Things is born to address this problem through the standard protocols responsible for the success of the Web. But a greater contribution can be provided by standards of the Semantic Web. Semantic Web protocols grant univocal identification of resources and representation of data in a way that information is machine understandable and computable and such that information from different sources can be easily aggregated. Semantic Web technologies are then interoperability enablers for the IoT. This Thesis investigates how to employ Semantic Web protocols in the IoT, to realize the Semantic Web of Things (SWoT) vision of an interoperable network of applications. Part I introduces the IoT, Part II investigates the algorithms to efficiently support the publish/subscribe paradigm in semantic brokers for the SWoT and their implementation in Smart-M3 and SEPA. The preliminary work toward the first benchmark for SWoT applications is presented. Part IV describes the Research activity aimed at applying the developed semantic infrastructures in real life scenarios (electro-mobility, home automation, semantic audio and Internet of Musical Things). Part V presents the conclusions. A lack of effective ways to explore and debug Semantic Web datasets emerged during these activities. Part III describes a second Research aimed at devising of a novel way to visualize semantic datasets, based on graphs and the new concept of Semantic Planes.La presenza massiva di dispositivi dotati di capacità computazionale e connettività sta alimentando la diffusione di un nuovo paradigma nell'ICT, conosciuto come Internet of Things. L'IoT è caratterizzato dai cosiddetti smart object che interagiscono, cooperano e reagiscono alle informazioni a loro disponibili per fornire servizi agli utenti. La diffusione dell'IoT su così tante aree è la testimonianza di un interesse mondiale da parte di ricercatori appartenenti sia al mondo accademico che a quello industriale. La Ricerca ha portato alla nascita di tecnologie e protocolli progettati per rispondere ai diversi bisogni degli scenari emergenti, rendendo difficile sviluppare applicazioni interoperabili. Il Web of Things (WoT) è nato per rispondere a questi problemi tramite l'adozione degli standard che hanno favorito il successo del Web. Ma un contributo maggiore può venire dal Semantic Web of Things (SWoT). Infatti, i protocolli del Semantic Web permettono identificazione univoca delle risorse e una rappresentazione dei dati tale che le informazioni siano computabili e l'informazione di differenti fonti facilmente aggregabile. Le tecnologie del Semantic Web sono quindi degli interoperability enabler per l'IoT. Questa Tesi analizza come adottare le tecnologie del Semantic Web nell'IoT per realizzare la visione del SWoT di una rete di applicazioni interoperabile. Part I introduce l'IoT, Part II analizza gli algoritmi per supportare il publish-subscribe nei broker semantici e la loro implementazione in Smart-M3 e SEPA. Inoltre, viene presentato il lavoro preliminare verso il primo benchmark per applicazioni SWoT. Part IV discute l'applicazione dei risultati a diversi domini applicativi (mobilità elettrica, domotica, semantic audio ed Internet of Musical Things). Part V presenta le conclusioni sul lavoro svolto. La Ricerca su applicazioni semantiche ha evidenziato carenze negli attuali software di visualizzazione. Quindi, Part III presenta un nuovo metodo di rappresentazione delle basi di conoscenza semantiche basato sull’approccio a grafo che introduce il concetto di Semantic Plane

Yavaa: supporting data workflows from discovery to visualization

Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations