Visualisation of Linked Data – Reprise

Tracking #: 1479-2691

Authors: 
Aba-Sah Dadzie
Emmanuel Pietriga

Responsible editor: 
Pascal Hitzler

Submission type: 
Editorial
Abstract: 
Linked Data is a key component of the Semantic Web vision. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of what we now call the Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, inter-related datasets. Since its early days, Linked Data (LD) promised to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to this Web of data. But the advantages of following LD principles have now become even more apparent, as the boundary between the data available on the (online) Web and users’ own (personal) data gets increasingly fuzzy. Users in our increasingly data- and knowledge-driven world are increasingly dependent on applications that build upon the capability to transparently fetch heterogeneous yet implicitly connected data from multiple, independent sources. It has become clear that we must rethink how those users interact with the very large amounts of this complex, interlinked, multi-dimensional data, throughout its management cycle, from generation to capture, enrichment in use and reuse, and sharing beyond its original context. The design of user interfaces for LD, and more specifically interfaces that represent the data visually, play a central role in this respect. Well-designed visualisations harness the powerful capabilities of the human perceptual system, providing users with rich representations of the data. Combined with appropriate interaction techniques, they enable users to navigate through, and make sense of, large and complex datasets, help spot outliers and anomalies, recognise patterns, identify trends. Visualisation can provide effective support for confirming hypotheses, and favours deriving new insight. Visual analytics systems complement human cognitive and perceptual abilities with automated data processing and mining techniques. Such systems not only support the presentation of data, but processes for the full data management cycle as well, from data capture to analyis, enrichment and reuse. We use the term "value" with reference to Linked Data several times in this paper. However, whether we will in reality see the value of LD is directly impacted by the cost of realising this value. Cost in terms of time, the financial cost, human effort and skill – technological and domain, and other resources required to extract this value and make timely, effective use of it in different contexts, through the use of effective, intuitive visualisation. Linked Data has been referred to as a “broker” that may be used both to reduce cost and increase the value of today’s big data. It has also been identified as an “enabler” – enabling richer, more effective use of independent and related datasets. Contributors to this special issue on Linked Data visualisation confirm the potential for LD as a unifier and a bridge across large scale, complex, distributed, heterogeneous data and independent tools. By harnessing visualisation as a tool for exploratory discovery and basic to advanced analysis, the papers in this volume illustrate design and the construction of intuitive means for target end users, from a variety of domains and with limited to advanced knowledge of technology, to obtain new insight and enrich existing knowledge, as they follow links defined across Linked Data. We look forward to a growing library of shared knowledge and visualisation-driven tools that break down technological barriers, promoting instead richer exploration and intuitive, insightful analysis of users’ personal context, myriad, shared situations and complex problems captured in Linked Data, and enable end users to draw confident conclusions about data and situations and add value to their everyday, knowledge-driven tasks.
Full PDF Version: 
Tags: 
Reviewed

Decision/Status: 
Accept

Comments