Drag it together with Groupie: making RDF data authoring easy and fun for anyone

One of the foremost challenges towards realizing a “Read-write Web of Data” [3] is making it possible for everyday computer users to easily find, manipulate, create, and publish data back to the Web so that it can be made available for others to use. However, many aspects of Linked Data make authoring and manipulation difficult for “normal” (ie non-coder) end-users. First, data can be high-dimensional, having arbitrary many properties per “instance”, and interlinked to arbitrary many other instances in a many different ways. Second, collections of Linked Data tend to be vastly more heterogeneous than in typical structured databases, where instances are kept in uniform collections (e.g., database tables). Third, while highly flexible, the problem of having all structures reduced as a graph is verbosity: even simple structures can appear complex. Finally, many of the concepts involved in linked data authoring - for example, terms used to define ontologies are highly abstract and foreign to regular citizen-users.To counter this complexity we have devised a drag-and-drop direct manipulation interface that makes authoring Linked Data easy, fun, and accessible to a wide audience. Groupie allows users to author data simply by dragging blobs representing entities into other entities to compose relationships, establishing one relational link at a time. Since the underlying representation is RDF, Groupie facilitates the inclusion of references to entities and properties defined elsewhere on the Web through integration with popular Linked Data indexing services. Finally, to make it easy for new users to build upon others’ work, Groupie provides a communal space where all data sets created by users can be shared, cloned and modified, allowing individual users to help each other model complex domains thereby leveraging collective intelligence

An intelligent linked data quality dashboard

This paper describes a new intelligent, data-driven dashboard for linked data quality assessment. The development goal was to assist data quality engineers to interpret data quality problems found when evaluating a dataset us-ing a metrics-based data quality assessment. This required construction of a graph linking the problematic things identified in the data, the assessment metrics and the source data. This context and supporting user interfaces help the user to un-derstand data quality problems. An analysis widget also helped the user identify the root cause multiple problems. This supported the user in identification and prioritization of the problems that need to be fixed and to improve data quality. The dashboard was shown to be useful for users to clean data. A user evaluation was performed with both expert and novice data quality engineers

Implementation and Deployment of a Library of the High-level Application Programming Interfaces (SemSorGrid4Env)

The high-level API service is designed to support rapid development of thin web applications and mashups beyond the state of the art in GIS, while maintaining compatibility with existing tools and expectations. It provides a fully configurable API, while maintaining a separation of concerns between domain experts, service administrators and mashup developers. It adheres to REST and Linked Data principles, and provides a novel bridge between standards-based (OGC O&M) and Semantic Web approaches. This document discusses the background motivations for the HLAPI (including experiences gained from any previously implemented versions), before moving onto specific details of the final implementation, including configuration and deployment instructions, as well as a full tutorial to assist mashup developers with using the exposed observation data

Connecting the dots: a multi-pivot approach to data exploration

The purpose of data browsers is to help users identify and query data effectively without being overwhelmed by large complex graphs of data. A proposed solution to identify and query data in graph-based datasets is Pivoting (or set-oriented browsing), a many-to-many graph browsing technique that allows users to navigate the graph by starting from a set of instances followed by navigation through common links. Relying solely on navigation, however, makes it difficult for users to find paths or even see if the element of interest is in the graph when the points of interest may be many vertices apart. Further challenges include finding paths which require combinations of forward and backward links in order to make the necessary connections which further adds to the complexity of pivoting. In order to mitigate the effects of these problems and enhance the strengths of pivoting we present a multi-pivot approach which we embodied in tool called Visor. Visor allows users to explore from multiple points in the graph, helping users connect key points of interest in the graph on the conceptual level, visually occluding the remainder parts of the graph, thus helping create a road-map for navigation. We carried out an user study to demonstrate the viability of our approach

Linked Data and Spatial Data Infrastructures

Spatial Data Infrastructures (SDIs) such as AuScope Grid and INSPIRE are being planned and built using discovery, access and processing components based on a services model. While the principle of distribution and delegation using the internet is a major step forward from traditional data warehouses and private collections, the query-oriented interaction paradigm is merely evolutionary, compared with traditional access systems designed for expert users. In contrast, the success and scalability of the world wide web has been based on hypertext, in which browsing is the key mode of interaction, supported by Universal Resource Identifiers (URIs). Linked Data has been proposed as the bridge from the browseable web to the deep web of technical data. Linked Data is still based on web-pages (usually HTML) for user interactions, but supported by Resource Description Framework (RDF) for richer link semantics. Links can resolve to datasets in legacy file formats which thus serve as leaf-nodes, but can also be part of the web of resources. Key standards used in SDI were designed on Linked Data principles, even before the name was coined. For instance, Geography Markup Language (GML) is essentially an RDF/XML application. Thus, in principle, SDIs should integrate seamlessly into the web of linked data. There are, however, a number of issues to consider or resolve in order to bring this about. These include: the definition of 'resource' in the context of databases that are accessed as projected subsets by query, accessed through web-service interfaces; multiple representations of the same feature from different services to support different applications; semantics embedded in structured representations expressed in non-RDF XML forms; standard vocabulary and identifier services.JRC.H.6-Digital Earth and Reference Dat

Exploring user and system requirements of linked data visualization through a visual dashboard approach

One of the open problems in SemanticWeb research is which tools should be provided to users to explore linked data. This is even more urgent now that massive amount of linked data is being released by governments worldwide. The development of single dedicated visualization applications is increasing, but the problem of exploring unknown linked data to gain a good understanding of what is contained is still open. An effective generic solution must take into account the user’s point of view, their tasks and interaction, as well as the system’s capabilities and the technical constraints the technology imposes. This paper is a first step in understanding the implications of both, user and system by evaluating our dashboard-based approach. Though we observe a high user acceptance of the dashboard approach, our paper also highlights technical challenges arising out of complexities involving current infrastructure that need to be addressed while visualising linked data. In light of the findings, guidelines for the development of linked data visualization (and manipulation) are provided