Hasso-Plattner-InstitutSDG am HPI
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Knowledge Graphs meet Language Models (Sommersemester 2022)

Dozent: Prof. Dr. Felix Naumann (Information Systems) , Nitisha Jain (Information Systems) , Alejandro Sierra Múnera (Information Systems)
Website zum Kurs: https://hpi.de/teaching/current-courses/ss-22/knowledge-graphs-meet-language-models.html

Allgemeine Information

  • Semesterwochenstunden: 2
  • ECTS: 3
  • Benotet: Ja
  • Einschreibefrist: 01.04.2022 - 30.04.2022
  • Lehrform: Seminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch
  • Maximale Teilnehmerzahl: 12

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
Data Engineering MA
  • 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

Beschreibung

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.

Voraussetzungen

  • Good understanding of machine learning and neural network

Literatur

  • 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/

Lern- und Lehrformen

Students will learn to...

  • Read and understand scientific publications
  • Analyze and summarize research contributions

Leistungserfassung

  • Paper presentation 30%
  • Final poster and presentation 70%

Termine

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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