This course will cover the foundations, techniques and applications of Knowledge Graphs (KGs). The course will start by surveying the state of the art. Then it will focus on advanced topics with a focus on spatio-temporal KGs and their applications in Human-in-the-loop applications. Various case studies will be analyzed, encompassing the integration with IOT sensor streams.
Day 1: Foundations, state-of-the-art of knowledge graph (KG)
Day 2: KG construction, representation, querying, and reasoning
Day 3: Spatio-Temporal KG (STKG), representation, use cases, applications
Day 4: KG and human-in-the-loop fundamentals, data integration, visualization, use cases
Day 5: IoT and KG, sensor data, use cases, applications. On the final day of the lecture, we will conduct the assessment.
A minimum of 75% attendance is required.
Teaching methods
1. Lectures: provide the theoretical foundation. Lectures will be delivered via interactive slides.
2. Practical: hands-on through different software tools.
3. Use case discussions and problem-solution.
Students must carry a regular laptop for hands-on sessions.
Assessment methods
Group/ individual projects, presentations
Bibliography
1. Aidan Hogan, Claudio Gutierrez, Michael Cochez, et al. Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. Springer Cham, 2022. pp. XIX, 237. https://doi.org/10.1007/978-3-031-01918-0
2. Dieter Fensel , Umutcan Şimşek , Kevin Angele, et al. Knowledge Graphs: Methodology, Tools and Selected Use Cases. Springer Cham. 2020. pp. XVI, 148. https://doi.org/10.1007/978-3-030-37439-6
3. Peng, C., Xia, F., Naseriparsa, M. et al. Knowledge Graphs: Opportunities and Challenges. Artif Intell Rev 56, 13071–13102 (2023). https://doi.org/10.1007/s10462-023-10465-9
4. K. Liang et al., "A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 9456-9478, Dec. 2024, doi: https://doi.org/10.1109/TPAMI.2024.3417451.
5. Ruiyi Yang, Flora D. Salim, Hao Xue. SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding. WWW '24: Proceedings of the ACM Web Conference 2024. Association for Computing Machinery, New York, NY, USA. pp. 551 - 559. https://doi.org/10.1145/3589334.3645441
6. Tyler Bikaun, Michael Stewart, Wei Liu (2024). CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion. https://arxiv.org/abs/2405.03932
7. Carlos Ramonell, Rolando Chacón, and Héctor Posada. (2023). Knowledge graph-based data integration system for digital twins of built assets. Automation in Construction, vol. 156, December 2023, 105109.
8. Zhiheng Zhao et al. (2022). Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation. Computers & Industrial Engineering, vol. 171, September 2022, 108454.
9. Li Cai et al. (2024). A Survey on Temporal Knowledge Graph: Representation Learning and Applications. https://arxiv.org/abs/2403.04782
