Course 4: Interaction with Linked Data
This learning pathway introduces techniques for the visualisation of RDF data, as well as statistical and machine learning techniques for the extraction of interesting patterns from data.
You can study the materials of this learning pathway at your own pace, as there is no predetermined start or end date.
1. Learning outcomes
By the end of this learning pathway you should have an understanding of:
- The process of extracting and transforming Linked Data for visualization.
- The range of visualization techniques available or different types of data
- The types of Linked Data visualization tools currently available
- How the Information Workbench  can be used to visualize data
- Approaches to visualizing the Linking Open Data cloud
- The use of dashboards to provide summary information about a dataset
- How semantics can be used to drive search and display search results
- Tools that can be used to search for semantic data
- How data can be aggregated and analysed statistically
- How machine learning can be used to identify patterns in a dataset
2. Linked Data Visualization
Learn about Linked Data visualization techniques that provide graphical representations of interesting information within a dataset.
3. Linked Data Search
Learn about techniques for conducting semantic search in Linked Data in order to identify data of interest.
4. Methods for Linked Data Analysis
Learn how statistical and machine learning techniques can be used to identify patterns in data.
5. Test your knowledge
How much have you learned from this learning pathway? Test your knowledge by completing the following exercise.
6. Further reading
If you are interested in more learning materials and resources about interaction with Linked Data, here are some suggestions that are relevant to this particular pathway:
 Brunetti , J.M.; Auer, S.; García, R. The Linked Data Visualization Model.
 Google Map API
 Dadzie, A.-S. and Rowe, M. (2011). Approaches to Visualising Linked Data: A Survey. Semantic Web surveys and applications, 2 (2), pp. 89-124.
 Tran, T., Herzig, D., Ladwig, G. SemSearchPro- Using semantics through the search process.
 Teevan , J., Dumais, S., Gutt. Z. Challenges for Supporting Faceted Search in Large, Heterogeneous Corpora like the Web
 “R for SPARQL” by Willen Robert van Hage & Tomi Kauppinen
 “Performing Statistical Methods on Linked Data” by Zapilko & Mathiak