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 [3] 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.

Watch Part I of the webinar 'Interaction with Linked Data' (45 minutes):

View the slides of this webinar:

Read Part I of Chapter 4 'Interaction with Linked Data':

HTML

iBook

ePUB

Kindle

3. Linked Data Search

Learn about techniques for conducting semantic search in Linked Data in order to identify data of interest.

Watch Part II of the webinar 'Interaction with Linked Data' (45 minutes):

View the slides of this webinar:

Read Part II of Chapter 4 'Interaction with Linked Data':

HTML

iBook

ePUB

Kindle

4. Methods for Linked Data Analysis

Learn how statistical and machine learning techniques can be used to identify patterns in data. 

Watch Part II of the webinar 'Interaction with Linked Data' (25 minutes in):

View the slides of this webinar:

Read Part III of Chapter 4 'Interaction with Linked Data':

HTML

iBook

ePUB

Kindle

5. Test your knowledge

How much have you learned from this learning pathway? Test your knowledge by completing the following exercise.

Execute this set of sample quiries in order to visualise the MusicBrainz dataset using the Information Workbench.

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:

[1] http://musicbrainz.fluidops.net

[2] http://en.wikipedia.org/wiki/Anscombe's_quartet

[3] Brunetti , J.M.; Auer, S.; García, R. The Linked Data Visualization Model.

[4] http://mbostock.github.io/protovis

[5] http://www.kottke.org/08/08/2008-movie-box-office-chart

[6] Google Map API

[7] http://www.wordle.net

[8] http://many-eyes.com

[9] http://sig.ma

[10] http://sindice.com

[11] http://www.fluidops.com/information-workbench

[12] http://musicbrainz.fluidops.net

[13] http://en.lodlive.it

[14] http://lodvisualization.appspot.com

[15] http://lod-cloud.net

[16] http://twitpic.com/17qj1h

[17] http://inkdroid.org/lod-graph

[18] Dadzie, A.-S. and Rowe, M. (2011). Approaches to Visualising Linked Data: A Survey. Semantic Web surveys and applications2 (2), pp. 89-124.

[19] https://www.google.com/webmasters/tools

[20] http://www.fluidops.com/ecloudmanager

[21] http://googlewebmastercentral.blogspot.de/2012/07/introducing-structured...

[22] Tran, T., Herzig, D., Ladwig, G. SemSearchPro- Using semantics through the search process.

[23] https://duckduckgo.com

[24] Teevan , J., Dumais, S., Gutt. Z. Challenges for Supporting Faceted Search in Large, Heterogeneous Corpora like the Web

[25] http://dblp.l3s.de

[26] http://dblp.uni-trier.de/db

[27] http://swoogle.umbc.edu

[28] http://watson.kmi.open.ac.uk

[29] http://swse.deri.org

[30] “R for SPARQL” by Willen Robert van Hage & Tomi Kauppinen

[31] “Performing Statistical Methods on Linked Data” by Zapilko & Mathiak

[32] http://www.r-project.org

[33] www.cs.waikato.ac.nz/ml/weka

[34] http://www.cip.ifi.lmu.de/~nickel/iswc2012-slides