106 research outputs found
A Survey on User Interaction with Linked Data
Since the beginning of the Semantic Web and the coining of the term Linked Data in 2006, more than one thousand datasets with over sixteen thousand links have been published to the Linked Open Data Cloud. This rising interest is fuelled by the benefits that semantically annotated and machine-readable information can have in many systems. Alongside this growth we also observe a rise in humans creating and consuming Linked Data, and the opportunity to study and develop guidelines for tackling the new user interaction problems that arise with it. To gather information on the current solutions for modelling user interaction for these applications, we conducted a study surveying the interaction techniques provided in the state of the art of Linked Data tools and applications developed for users with no experience with Semantic Web technologies. The 18 tools reviewed are described and compared according to the interaction features provided, techniques used for visualising one instance and a set of instances, search solutions implemented, and the evaluation methods used to evaluate the proposed interaction solutions. From this review, we can conclude that researchers have started to deviate from more traditional visualisation techniques, like graph visualisations, when developing for lay users. This shows a current effort in developing Semantic Web tools to be used by lay users and motivates the documentation and formalisation of the solutions encountered in the studied tools. Copyright (c) 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
A comparative study of state-of-the-art linked data visualization tools
Data visualization tools are of great importance for the exploration and the analysis of Linked Data (LD) datasets. Such tools allow users to get an overview, understand content, and discover interesting insights of a dataset. Visualization approaches vary according to the domain, the type of data, the task that the user is trying to perform, as well as the skills of the user. Thus, the study of the capabilities that each approach offers is crucial in supporting users to select the proper tool/technique based on their need. In this paper we present a comparative study of the state-of-the-art LD visualization tools over a list of fundamental use cases. First, we define 16 use cases that are representative in the setting of LD visual exploration, examining several tool's aspects; e.g., functionality capabilities, feature richness. Then, we evaluate these use cases over 10 LD visualization tools, examining: (1) if the tools have the required functionality for the tasks; and (2) if they allow the successful completion of the tasks over the DBpedia dataset. Finally, we discuss the insights derived from the evaluation, and we point out possible future directions
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Development of self-archiving tools to support archiving, analysis and re-use of qualitative data
The potential to share and re-use qualitative archived data has garnered much interest in recent years. This increased attention can be attributed mainly to advances in both data documentation standards and digital archiving technologies, which provide users with the ability to archive, share and disseminate qualitative research materials. However, there remain theoretical and epistemological barriers to and implications for the sharing and re-use of qualitative study data. One way to address these issues is by studying research practices (with practitioners’ active involvement), in combination with developing software tools that support digital archiving of qualitative studies. Semantic technologies, combined with metadata standards and documentation schemas have the potential to enhance qualitative data documentation, archiving and analysis. In fact, it has been established that data documentation is one of the key elements that enables data archiving. The use of appropriate standard documentation frameworks is crucial to data archives’ exposure and has a direct impact on the discoverability, search and retrieval of archived data. The technological aspect of this study has been the development of a self-archiving toolkit that makes use of such technologies. The purpose of this work was to allow users, with varying levels of research experience (e.g. from undergraduate student researchers up to more experienced senior researchers) to avail of the benefits offered by qualitative digital archiving. To complement the technological developments undertaken, the present study also explored the practices of different researchers: undergraduate student researchers, researchers involved in teaching research-oriented modules, as well as senior researchers. This exploration focused on the collection, organisation, analysis and presentation of qualitative data and how these relate to and can be supported by digital archiving to enable researchers to organise, disseminate, and visualise research collections
Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010
This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb.
UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010.
The overarching theme this year was “Global Challenges”, with specific focus on the following themes:
* Crime and Place
* Environmental Change
* Intelligent Transport
* Public Health and Epidemiology
* Simulation and Modelling
* London as a global city
* The geoweb and neo-geography
* Open GIS and Volunteered Geographic Information
* Human-Computer Interaction and GIS
Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond
Integrated topological representation of multi-scale utility resource networks
PhD ThesisThe growth of urban areas and their resource consumption presents a significant global
challenge. Existing utility resource supply systems are unresponsive, unreliable and costly.
There is a need to improve the configuration and management of the infrastructure networks
that carry these resources from source to consumer and this is best performed through analysis
of multi-scale, integrated digital representations. However, the real-world networks are
represented across different datasets that are underpinned by different data standards, practices
and assumptions, and are thus challenging to integrate.
Existing integration methods focus predominantly on achieving maximum information
retention through complex schema mappings and the development of new data standards, and
there is strong emphasis on reconciling differences in geometries. However, network topology
is of greatest importance for the analysis of utility networks and simulation of utility resource
flows because it is a representation of functional connectivity, and the derivation of this
topology does not require the preservation of full information detail. The most pressing
challenge is asserting the connectivity between the datasets that each represent subnetworks of
the entire end-to-end network system.
This project presents an approach to integration that makes use of abstracted digital
representations of electricity and water networks to infer inter-dataset network connectivity,
exploring what can be achieved by exploiting commonalities between existing datasets and data
standards to overcome their otherwise inhibiting disparities. The developed methods rely on the
use of graph representations, heuristics and spatial inference, and the results are assessed using
surveying techniques and statistical analysis of uncertainties. An algorithm developed for water
networks was able to correctly infer a building connection that was absent from source datasets.
The thesis concludes that several of the key use cases for integrated topological representation
of utility networks are partially satisfied through the methods presented, but that some
differences in data standardisation and best practice in the GIS and BIM domains prevent full
automation. The common and unique identification of real-world objects, agreement on a
shared concept vocabulary for the built environment, more accurate positioning of distribution
assets, consistent use of (and improved best practice for) georeferencing of BIM models and a
standardised numerical expression of data uncertainties are identified as points of development.Engineering and Physical Sciences Research Council
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