a visual exploration workflow as enabler for the exploitation of linked open data
DESCRIPTION
Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain.However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers understanding the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and exploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the workflow’s potential impact on data consumers which bridges the offer as published Linked Open Data, and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70% of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to understand the potential of Linked Open DataTRANSCRIPT
A Visual Exploration Workflow as Enablerfor the Exploitation of Linked Open Data
Laurens De VochtA.Dimou, J. Breuer, M. Van Compernolle, R. Verborgh,
E. Mannens, P. Mechant, R. Van de Walle
Investigate demandHow todiscover, explore and analyze available published datareuse and exchange of resources
Linked Open Data
Advantagea substantial role in contexts like e.g.digital libraries and archivesideal to reveal links between resources
Disadvantagelack of understanding of the semantic technology limits non-semantic Web expertsto optimally interpret and query Linked Data
1. Defining the Workflow2. Visualization3. Evaluation4. Discussion and Conclusions
Agenda
1. Defining the Workflow
A visual workflow for resources represented
as Linked Open Datafor exploitation, discovery and analysis
of the Web of Data using information exploration techniques
Narrowing
The user gets familiar with the dataset
no explicit assumption regarding the dataset
the dataset itself reveals its underlying model
and the relationships between its resources
Narrowing
Initial overview the broader concepts are demonstrated
Narrowing views narrow the broader concepts.each narrowing view relies on grouping and aggregating resources based on their types and properties
Coordinated View
Most detailed resources cannot be further decomposed
a certain resource or the links between two resources
Coordinates transition between narrowing and broadening views
Broadening
The user is familiar with the dataset
Users explore the dataset on their ownfind novel relations between resources
Views are not limited to the data of the dataset but relevant links to resources of other datasets are also revealed and visualized
2. Visualization
3. Evaluation
Results
Case study: Academic Library Metadata
User perceived goals
Explorability and Usefulness
Complexity and Learnability
Overall results
User perceived goals
Explorability and Usefulness
Complexity and Learnability
Overall Results
4. Discussion and Conclusions
Discussion
ConclusionsUser interfaces based on graph visualizations offer unique, multifaceted experience
when combined with techniques for information explorationand enhanced with optimized search in Linked Data.
enables users to view and navigate through combined aspects of research data
to come up spontaneously with observations whose reasoning can be directly investigated
It contributes to having more users familiar with Linked data and thus an increased demand.
Contact
http://www.resxplorer.orghttp://ewi.mmlab.be/academic
@laurens_d_v #[email protected]://slideshare.net/laurensdvhttp://semweb.mmlab.be/