This course introduces the foundations, techniques, and applications of Knowledge Graphs (KGs). It begins with KG fundamentals, their evolution, and current state-of-the-art theories and practices. The course then explores advanced topics, including personal knowledge graphs, spatio-temporal KGs, and their roles in human-in-the-loop systems. It also examines enterprise KGs and practical implementation strategies. Throughout the course, various case studies will be discussed, including examples involving integration with IoT sensor streams.
Day 1: Foundations, state-of-the-art of knowledge graph (KG)
Day 2: KG construction, representation, constraint mechanism, querying, and reasoning
Day 3: Domain and 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. on sessions.
Assessment methods
Group/ individual projects, presentations
Bibliography
1. Serles, U. and Fensel, D. An Introduction to Knowledge Graphs. Springer Cham, 2024. Pp. XXIV, 434. https://doi.org/10.1007/978-3-031-45256-7
2. Sequeda, J. and Lassila, Ora. Designing and Building Enterprise Knowledge Graphs. Springer, 2021.
