Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users. Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph. The graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., “this shop is located in Aachen”). Furthermore, it may contain context information (e.g., the source of some information) and schema information or background knowledge (e.g., “shops have opening hours”). This seminar consists of an introductory course on Knowledge Graphs. You will give a short outline presentation on your assigned topic to set overview and expectations about the paper you’re going to write. The main deliverable of the seminar is a paper that describes the state of the art of your assigned topic. While you do not need to contribute original research, your task is to show the scientific competences of literature research, presentation of a research question and understanding and putting relevant papers into context. Furthermore, you are asked to critically assess and compare strengths or challenges of existing solutions. You will review your peer’s papers and give relevant feedback to enhance your scientific writing skills. You will present your paper in a final presentation in a block seminar at the end of the semester.Deliverables of this seminar
| Type | Seminar |
| Term | WS 2023 |
| Mentor(s) |
Stefan Decker |
| Assistant(s) |
Sulayman K. Sowe Yongli Mou Diego Collarana |