CS7IS1 – Knowledge and Data Engineering

Module CodeCS7IS1
Module NameKnowledge and Data Engineering
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 1
Module Coordinator/s  Prof. Declan O’Sullivan

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Compare and contrast different approaches to modelling information and knowledge (ISLO2);
  2. Model information and produce rich semantic models and ontologies (ISLO1, ISLO4);
  3. Survey the state of the art in semantic technologies and applications;
  4. Demonstrate a clear understanding of the principles underlying information interoperability and transformation;
  5. Apply semantic modelling and transformation techniques to a range of applied problems;
  6. Use sophisticated querying approaches to facilitate distributed information retrieval and aggregation.

Module Content

The module is designed to explore the management, delivery and inter-operability of knowledge, information and data through knowledge and data engineering. The module encourages students to perceive the challenges, technologies and solutions, in handling distributed, multi modal, heterogeneous information and knowledge models and using those models to drive adaptation within applications and systems. The module focuses on advanced technologies (in particular semantic web technologies), to provide adaptive, agile handling of heterogeneous, ubiquitous information. The module includes such areas as integration of heterogeneous information repositories, schema (RDF) and semantic (e.g. ontology) representation and querying.

The main themes of the module are:

  • Standards-based approaches to publishing, sharing and reusing structured data or knowledge on the web;
  • Managing, integrating and transforming disparate information from heterogeneous sources;
  • Representing, managing, and reasoning about semantics of information (and services).

Specific topics addressed in this module include:

  • Semantic Web/Linked Data/Knowledge Graphs;
  • Semantic Model Design;
  • Representing Semantics in metadata;
  • Semantic based querying in a distributed environment (SPARQL);
  • Semantic Interoperability/Mapping.

Teaching and Learning Methods

The module will use a mixture of lectures, labs and assessment tasks to be undertaken as an individual and within groups.

Assessment Details

100% Continuous Assessment.

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
Individual ResearchIndividual Research TaskLO1, LO335%Week 1Mid Week 3
Group ProjectGroup Development ProjectLO2, LO4, LO5, LO650%Week 4Mid Week 10
Individual PortfolioOngoing Topic Related TasksAll 15%Week 1Mid Week 11

Reassessment Details

Individual Project (100%).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:22 hours
Lecture11 hours
Laboratory/Tutorial5 hours
Seminar6 hours
Other0 hours
Independent Study (outside scheduled contact hours), broken down by:98 hours
Preparation for classes and review of material (including preparation for examination, if applicable)49 hours
Completion of assessments (including examination, if applicable)49 hours
Total Hours120 hours

Recommended Reading List

  • Semantic Web Programming, by J. Hebeler, M. Fisher, R. Blace and A. Perez-Lopez, Wiley 2009.
  • Programming the Semantic Web, by T. Segaran, C. Evans and J. Taylor, O’Reilly 2009.
  • Linked Data: Evolving the Web into a Global Data Space, by T. Heath and C. Bizer, Morgan & Claypool, 2011.

Module Pre-requisites

Prerequisite modules: Database and information modelling modules.

Other/alternative non-module prerequisites: N/A

Module Co-requisites

N/A

Module Website

Blackboard