- Type: Seminar
- Teacher: Simon Razniewski (lecturer)
- Credits: 7 ECTS credits
- Registration: Via https://seminars.cs.uni-saarland.de
- Covid: Course participation is possible both offline (preferred), but also online-only.
- Topic Description: Commonsense knowledge (CSK) is critical for building versatile intelligent applications. In delineation from encyclopedic knowledge, which is centered on named entities like Trump, Paris, or FC Barcelona, commonsense is used to refer to properties, traits and relations between general concepts, such as elephants, universities, or painters. Machine-readable collections of CSK are crucial to enable question answering and natural conversation about the world, e.g., by enabling the agent to proactively communicate, identify likely answers, and detect implausible statements and conditions. In this seminar we will study foundational and recent topics around commonsense knowledge extraction and consolidation.
The seminar is a block seminar and will take place on two consecutive days in winter 2021. There will also be two meetings at the beginning of the semester, for which participation is mandatory.
- A psychological view on commonsense knowledge
- Reference 1: TBD
- Computational modelling of commonsense
- Time and Causality
- Quantitative knowledge
- Social stereotypes
- Commonsense explanations
- Physical commonsense
(own topic suggestions are welcome as well!)
- October 30: Participant selection
- November 5, 10am-12: "Introduction to commonsense" lecture
- November 12, 10am-12: "Seminar survival skills" lecture + 1st deliverable due + topic assignment
- December 12: 2nd deliverable due
- December 14-18: Meetings with advisor
- January 14: 3rd deliverable due
- January 28: 4th deliverable due
- February 11: 5th deliverable due
- March 4: Block seminar
There will be a total of 6 deliverables. To pass the course, all have to be submitted on time. Percentages in brackets denote contribution to final grade.
- Max. 3 page writeup on short open questions (5%)
- Outline of report (5%)
- Report 1st revision (0%)*
- Reviews on two other reports (10%)
- Reports 2nd revision (40%)
- Presentation (40%)
* 1st revision is not graded, but the prime chance to obtain feedback from advisor and peers.