Truth Discovery Algorithms
Description
In our modern world, we face an abundance of information provided by a variety of sources. However, not every information source is reliable and some even try to manipulate us to false beliefs. Truth discovery deals with the problem of finding the true value among a number of conflicting information about an entity.
In this seminar, we want to learn about the different challenges in the research area of truth discovery, by studying state of the art scientific papers. This course is a master seminar that focusses on reading, presenting, discussing and understanding research papers.
Time Table
When: Thursday 13:30 online (until further notice - if we can meet at HPI again, we will meet at Campus II, at Building F-2.11).
Date | Topic | Slides |
| 30.04.2020 | Introduction and topic selection | Download |
| 14.05.2020 | Flash talks | |
| 28.05.2020 | General Approach Comparison | |
| 04.06.2020 | Unstructured Data | |
| 25.06.2020 | Streaming Data | |
| 02.07.2020 | Source Dependencies | |
| 16.07.2020 | Multi-Truth Discovery | |
| 23.07.2020 | Final meeting |
Final report submission deadline: 24.07.2020
Literature
The seminar uses the following survey as a starting point: https://dl.acm.org/doi/10.1145/2897350.2897352
Organization
General
- Seminar for master students
- Language of instruction: English
- Maximum number of participants: 10
Topics will be presented in the first Session (30.04.2020). For topic assignments, participants will have to write us an E-Mail by Friday evening 01.05.2020 in which they can give preferences for up to 3 of the presented topics. After Friday, the topics will be assigned by us. In case of too many applicants for a specific topic, we will decide randomly.
Grading
In the seminar, each participant will give a presentation about a predefined topic within the research area of truth discovery and write a short report. The final grade consists of the following three parts:
- Presentation (45%)
- Written report (35%)
- Discussion in the seminar sessions (20%)