Indicative topics include:
• Web of data and Knowledge Graphs
• Analysing graph data with querying
• Ontologies for structuring and reasoning with data
• Integrating legacy data as Knowledge Graphs
• Graph Mining
• Graph Pattern Matching Methods
• Graph layout techniques
Total contact hours: 28 hours
Private study hours: 122 hours
Total study hours: 150 hours
Main assessment methods:
2 coursework (15 hours & 15 hours) (25% & 25%)
2 hour unseen exam (50%)
Reassessment methods:
Like for like.
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
Dean Allemang and Jim Hendler (2011). Semantic Web for the Working Ontologist, 2nd edn, Morgan Kaufmann.
Grigoris Antoniou & Frank van Harmelen (2012). A Semantic Web Primer, 3rd edn, MIT Press
Bob DuCharme (2013). Learning SPARQL, 2nd edn. O'Reilly.
David Wood, Marsha Zaidman and Luke Ruth (2013). Linked Data: Structured Data on the Web. Manning Publications.
Pascal Hitzler, Markus Kroetzsch and Sebastian Rudolph (2009). Foundations of Semantic Web technologies. CRC Press.
Roberto Tamassia (2016). Handbook of Graph Drawing and Visualization (Discrete Mathematics and Its Applications). CRC Press.
Diane J. Cook & Lawrence B. Holder (2006). Mining Graph Data. Wiley
On successfully completing the module students will be able to:
1. Understand the web of data and how it facilitates sharing, use of and reasoning about data
2. Understand how to query graph data
3. Have developed a critical awareness of state-of-the-art techniques for data integration
4. Apply graph mining techniques
5. Use graph layout methods to visualize network information
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