Knowledge graphs (KGs) have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.*
In KGs nodes correspond to entities, and edges correspond to relations between two connected entities. An edge in a KG represents a fact stored in the form of “<subject><predicate><object>”, (e.g., “<Robert Pattinson><starred-in><Tenet>”).
Given these facts from data sources, we focus on analytics over dynamic KGs in this seminar. For example, implementing methods figuring out who belongs to which community in Hollywood.
In this seminar, KG analytics may comprise:
- Identify important (“central”) nodes or edges
- Connectivity analysis
- Structural characteristics, such as subgraph pattern
- Community detection
- Rule mining
- etc.
Students will choose a dataset/use-case and implement efficient analytics over dynamic KGs using current research methods and approaches.**
* Hogan, Aidan, et al. "Knowledge graphs." ACM Computing Surveys (CSUR) 54.4 (2021): 1-37. https://dl.acm.org/doi/pdf/10.1145/3447772
** Besta, Maciej, et al. "Practice of streaming processing of dynamic graphs: Concepts, models, and systems." IEEE Transactions on Parallel and Distributed Systems (2021). https://arxiv.org/pdf/1912.12740.pdf
