|Module Name||Knowledge and Data Engineering|
|ECTS Weighting ||5 ECTS|
|Semester Taught||Semester 1|
|Module Coordinator/s||Prof. Declan O’Sullivan|
Module Learning Outcomes (22/23)
On successful completion of this module, students will be able to :
- Compare and contrast different approaches to modelling information and knowledge (ISLO2);
- Model information and produce rich semantic models and ontologies (ISLO1, ISLO4);
- Survey the state of the art in semantic technologies and applications;
- Demonstrate a clear understanding of the principles underlying information interoperability and transformation;
- Apply semantic modelling and transformation techniques to a range of applied problems;
- Use sophisticated querying approaches to facilitate distributed information retrieval and aggregation.
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.
100% Continuous Assessment.
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of Total||Week Set||Week Due|
|Individual Research||Individual Research Task||LO1, LO3||35%||Week 1||Mid Week 3|
|Group Project||Group Development Project||LO2, LO4, LO5, LO6||50%||Week 4||Mid Week 10|
|Individual Portfolio||Ongoing Topic Related Tasks||All||15%||Week 1||Mid Week 11|
Individual Project (100%).
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||22 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 Hours||120 hours|
Recommended Reading List
- Knowledge Graph Book by Aidan Hogan, 2020
- Foundations of Semantic Web Technologies
- 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
- Learning SPARQL by Bob DuCharme
Prerequisite modules: Database and information modelling modules, Programming modules.
Other/alternative non-module prerequisites: N/A