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Journal Articles Open Journal Of Semantic Web Year : 2017

Visualisation of Linked Data - Reprise

Aba-Sah Dadzie
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Abstract

Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. 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 this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, interrelated datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data.
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Dates and versions

hal-01406437 , version 1 (01-12-2016)

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Aba-Sah Dadzie, Emmanuel Pietriga. Visualisation of Linked Data - Reprise. Open Journal Of Semantic Web, 2017, 8 (1), pp.1 - 21. ⟨10.3233/SW-160249⟩. ⟨hal-01406437⟩
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