Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Knowledge Modeling and Reasoning | |||
| Reference | CMM304 | Version | 1 |
| Created | January 2024 | SCQF Level | SCQF 11 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To enable students to design, implement, and use knowledge graphs using of appropriate symbolic AI technologies and understand their role in AI systems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Design solutions based on the technologies and knowledge representation formalisms of that underpin symbolic AI. |
| 2 | Make judgements on the technical architectures for incorporating symbolic knowledge representations in AI systems. |
| 3 | Synthesise methods for reasoning and querying semantic knowledge into working solutions. |
| 4 | Appraise the quality of knowledge graphs. |
| Indicative Module Content |
|---|
| Principles of knowledge representation and symbolic AI, knowledge graphs, deductive knowledge, RDF, OWL, ontologies, querying knowledge graphs via SPARQL, quality assessment, linked data principles. |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures, which precede each lab session. In the lab sessions the students will learn practical aspects of using knowledge representation and reasoning methods in AI systems. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | A coursework normally typically consisting of designing, development and evaluation of knowledge graphs and related technologies. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighing of C1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1 |
| B | The student needs to achieve a B in C1 |
| C | The student needs to achieve a C in C1 |
| D | The student needs to achieve a D in C1 |
| E | The student needs to achieve an E in C1 |
| F | The student needs to achieve an F in C1 |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann (2021) Knowledge Graphs, Synthesis Lectures on Data, Semantics, and Knowledge, No. 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. |
| 2 | Tom Heath and Christian Bizer (2011) Linked Data: Evolving the Web into a Global Data Space (1st edition). Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool. |
| 3 | Kejriwal, Mayank (2019) Domain-specific knowledge graph construction. Springer |