Knowledge Graphs meet Language Models (Sommersemester 2022)
Lecturer:
Prof. Dr. Felix Naumann
(Information Systems)
,
Nitisha Jain
(Information Systems)
,
Alejandro Sierra Múnera
(Information Systems)
Course Website:
General Information
- Weekly Hours: 2
- Credits: 3
- Graded:
yes
- Enrolment Deadline: 01.04.2022 - 30.04.2022
- Teaching Form: Seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: English
- Maximum number of participants: 12
Programs, Module Groups & Modules
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
Description
Knowledge Graphs represent information from real-world entities and their relationships in a graph structure. Their construction, representation, and completion have been active research topics in the last few years.
On the other hand, information have also been represented through natural language in vast amounts of documents throughout the history of humanity and recently large language models have been trained to embed this textual information in dense vector spaces.
In this seminar we are going to study the basic concepts of knowledge graphs (creation and completion) and language model training, and then we will concentrate on recent research that have tried to combine both approaches.
Requirements
- Good understanding of machine learning and neural network
Literature
- Gerhard Weikum, Xin Luna Dong, Simon Razniewski and Fabian Suchanek (2021), "Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases", Foundations and Trends® in Databases: Vol. 10: No. 2-4, pp 108-490. http://dx.doi.org/10.1561/1900000064
- Dan Jurafsky and James H. Martin, "Speech and Language Processing" (3rd ed. draft) https://web.stanford.edu/~jurafsky/slp3/
Learning
Students will learn to...
- Read and understand scientific publications
- Analyze and summarize research contributions
Examination
- Paper presentation 30%
- Final poster and presentation 70%
Dates
| Date | Topic |
| April 18 | Holiday |
| April 25 | Organization & Preview |
| May 2 | Introduction session |
| May 9 | Paper discussion |
| May 16 | Paper discussion |
| May 23 | Paper discussion |
| May 30 | Paper discussion |
| June 6 | Holiday |
| June 13 | Paper discussion |
| June 20 | Paper discussion + Introduction to Part 2 |
| July 11 | Paper consultation |
| July 25 | Final Poster Session |
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