Advanced Database Systems(INF 642)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 642 | Advanced Database Systems | 1 | 3 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Doctoral Degree |
| Course Instructor(s) | Sultan Nezihe TURHAN sturhan@gsu.edu.tr (Email) |
| Assistant | |
| Objective | The primary objective of this course is to train research-oriented doctoral students capable of contributing to the theoretical and methodological advancement of graph database systems. The course aims to develop advanced expertise in graph data models, Semantic Web technologies, and neural-symbolic integration frameworks, while strengthening students’ abilities in formal reasoning, complexity analysis, and algorithmic design. It further seeks to equip students with the necessary theoretical and practical foundations to produce high-quality scientific research and publications in top-tier venues. |
| Content | The course covers the theoretical and methodological foundations of graph database systems, with a particular emphasis on formal semantics, query expressiveness, and computational complexity. It begins with core concepts such as graph data models, first-order logic, datalog, and graph homomorphisms, and progresses to the formal framework of the Semantic Web, including RDF semantics and advanced SPARQL query processing and optimization. The course further explores knowledge representation and reasoning through description logics, ontology-based inference, and inconsistency handling mechanisms. A dedicated component focuses on neural-symbolic integration, including differentiable reasoning over graphs and the theoretical limits of graph neural networks. Advanced topics include knowledge graph construction, information extraction models, automated ontology learning, and statistical relational learning. The course also addresses distributed graph systems, covering query processing, consistency models, and consensus mechanisms. Finally, emerging research directions such as temporal knowledge graphs, multimodal data integration, and cross-modal reasoning are examined, providing a comprehensive foundation for doctoral-level research. |
| Course Learning Outcomes |
Upon successful completion of this course, students will be able to: 1. Analyze the theoretical foundations of graph database systems, including formal semantics, query expressiveness, and computational complexity. 2. Evaluate and compare different knowledge representation and reasoning frameworks, including RDF, OWL, and description logics. 3. Design and optimize advanced graph queries using formal query languages such as SPARQL and Datalog. 4. Apply formal reasoning techniques to solve problems related to ontology inference, inconsistency handling, and logical entailment. 5. Critically assess neural-symbolic approaches, including graph neural networks, in terms of expressiveness, limitations, and theoretical soundness. 6. Develop and implement advanced knowledge graph construction pipelines using formal and statistical methods. 7. Analyze distributed graph systems with respect to consistency models, query processing, and scalability challenges. 8. Synthesize emerging research directions in graph databases, including temporal and multimodal knowledge graphs. 9. Conduct independent research by formulating research problems, reviewing scientific literature, and producing publishable results. |
| Teaching and Learning Methods | The course adopts a research-oriented and student-centered teaching approach that combines theoretical rigor with active learning and scientific inquiry. Teaching methods include advanced lectures focused on formal foundations and state-of-the-art developments, complemented by in-depth discussions of scientific literature. Students engage in critical reading and presentation of research papers, fostering analytical and evaluation skills. Weekly assignments are designed to reinforce theoretical concepts through problem-solving and formal analysis. The course also incorporates seminar-style sessions, where students actively participate in discussions and peer feedback processes. A significant component of the learning process is dedicated to independent research, in which students formulate research questions, design methodologies, and develop original contributions under instructor supervision. This integrated approach aims to bridge theory and research practice, preparing students for doctoral-level scientific work. |
| References |
1. Serles, U., & Fensel, D. (2024). An Introduction to Knowledge Graphs. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45256-7 2. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., & Neumaier, S. (2021). Knowledge Graphs. Springer Verlag. https://doi.org/10.2200/S01125ED1V01Y202109DSK022 3. Kejriwal, M., Knoblock, C. A., & Szekely, P. (2021). Knowledge Graphs: Fundamentals, Techniques, and Applications. MIT Press. https://doi.org/10.7551/mitpress/11382.001.0001 4. Additional and Recommended Readings : In addition to the core textbooks, students are encouraged to engage with recent high-impact research articles from leading conferences and journals. The list of selected papers is updated annually to reflect the latest developments and emerging research directions in graph databases, the Semantic Web, and neural-symbolic systems. |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Theoretical Foundations of Graph Databases |
| 2 | Semantic Web and RDF Theoretical Framework |
| 3 | SPARQL |
| 4 | Knowledge Representation and Reasoning Systems and OWL |
| 5 | Neural-Symbolic Integration Theory |
| 6 | Advanced Knowledge Graph Construction |
| 7 | Distributed Graph Systems Theory |
| 8 | Temporal knowledge graphs: time logic, versioning theory. |
| 9 | Multimodal integration; cross-modal reasoning frameworks. |
| 10 | |
| 11 | |
| 12 | |
| 13 | |
| 14 |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 6 | 50 |
| Contribution of final exam to overall grade | 1 | 50 |
| Toplam | 7 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 5 | 20 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 1 | 30 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 6 | 50 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 14 | 6 | 84 |
| Assignments | 5 | 2 | 10 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 1 | 10 | 10 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 1 | 30 | 30 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 176 | ||
| Total Workload / 25 | 7.04 | ||
| Credits ECTS | 7 | ||