Welcome to the Wiki of the courses "Databases and Information Systems" and "Information Retrieval" in the winter semester 2023/2024

Results of the official evaluation of this course

The course is given by Prof. Dr. Hannah Bast and assisted by Sebastian Walter. It takes place every Tuesday from 14:05 - 15:55 h in the seminar room 101-1-9/13 and on Zoom (meeting link, meeting ID: 668 8383 5704, passcode: IRWS23/24). The first lecture is on Tuesday 17.10.2023 and the last lecture is on Tuesday 06.02.2024. There will be no lectures on the Tuesdays 26.12.2023 and 02.01.2024 (Christmas break) and on two additional Tuesdays not determined yet. In total there will be 13 lectures.

The tutors for this course are Daniel Bindemann, Patrick Brosi, Johannes Kalmbach, Natalie Prange and Robin Textor-Falconi. The administrator of the supporting systems (Daphne, SVN, Forum, Jenkins) is Axel Lehmann.

Important Links

• Our course management system Daphne (login with your RZ user name and password).

• The forum for announcements and questions.

• The manual for how to ask questions on the forum.

• Our rules for the exercise sheets. Please read them carefully, completely and before you start working on the exercises. They are valid for all exercise sheets of this course.

• Information about Subversion (SVN) can be found here (in German) and here (in English); about some editors (including Vim) here (in German).

• The courses from previous semesters: Information Retrieval WS 22/23, Information Retrieval WS 21/22, Information Retrieval WS 19/20, Information Retrieval WS 18/19, Information Retrieval WS 17/18, Information Retrieval WS 16/17, Information Retrieval WS 15/16, Information Retrieval WS 13/14, Information Retrieval WS 12/13.

• The exams from previous semesters: WS 22/23, WS 21/22, WS 19/20, WS 18/19, WS 17/18, WS 16/17, WS 15/16, WS 13/14, WS 12/13.

• Here you find a list of the Python packages you should have installed in your environment. Alternatively you can download and use our Linux Image (for Virtualbox or VMWare) which already contains all packages.

• A PyTorch cheatsheet can be found here. An overview over basic tensor usage within PyTorch can be found here. If you are already familiar with NumPy you can find information about how NumPy operations translate to PyTorch operations here.

• Here you can find a link to a video tutorial about how to work on the exercise sheets with our course system Daphne and SVN.

• Here you can book individual time slots with your tutor.

Lecture Slides, Video Recordings, Exercise Sheets, and Code

For visitors from outside the University of Freiburg: You can freely access all the course materials below, except the solutions, by replacing /svn/ by /svn-public/ in the URL.

• Lecture 1, Tuesday, October 17, 2023 (Introduction, Inverted Index, Zipf's Law):
  Video recording (MP4 Download), Slides, Exercise Sheet 1, Code from the lecture, Code template for ES1, Dataset for ES1 (115,454 movies with title + description + 3 columns with scores that you can ignore for this sheet). Note: If the dataset download does not start automatically when clicking the link, copy the link address and paste it in a new tab or download it using the wget command. Solution for ES1

• Lecture 2, Tuesday, October 24, 2023 (Ranking and Evaluation):
  Video recording (MP4 Download), Slides, Exercise Sheet 2, Code template for ES2, Table to enter your benchmark results, Training benchmark, Testing benchmark, Solution for ES2

• Lecture 3, Tuesday, October 31, 2023 (Database basics, SQLite3, SQL Introduction):
  Video recording (MP4 Download), Slides, Exercise Sheet 3, SQL from the lecture, Template for ES3, Solution for ES3

• Lecture 4, Tuesday, November 7 2023 (Query planning and joins):
  Video recording (MP4 Download), Slides, Exercise Sheet 4, Code template for ES4, TSV files (.tar.gz), Table to enter your query results, Solution for ES4

• Lecture 5, Tuesday, November 14 2023 (Merge join, more SQL, dynamic tables):
  Video recording (MP4 Download), Slides, Exercise Sheet 5, Code template for ES5, New TSV files (.tar.gz), Solution for ES5

• Lecture 6, Tuesday, November 21 2023 (Knowledge Graphs, SPARQL, SPARQL to SQL):
  Video recording (MP4 Download), Slides, Exercise Sheet 6, Code template for ES6, wikidata-simple.tsv (915 MB, 17'360'245 lines), wikidata-complex.tsv (3.4 GB, 60'022'981 lines), Solution for ES6

• Lecture 7, Tuesday, December 5 2023 (Fuzzy Search, Prefix Edit Dist, Q-Gram Index):
  Video recording (MP4 Download), Slides, Exercise Sheet 7, Code template for ES7, wikidata-entities.tsv (269 MB, 1'581'140 entities), wikidata-entities.100k.tsv (21 MB, 100'000 entities), Table to enter your query results, Solution for ES7

• Lecture 8, Tuesday, December 12 2023 (Web Applications 1):
  Video recording (MP4 Download), Slides, Exercise Sheet 8, Code template for ES8, wikidata-entities-new.tsv (UPDATED), wikidata-entities-new.100k.tsv (UPDATED), wikidata-properties.tsv (400 new triples for optional task 4), Solution for ES8 (without SPARQL-to-SQL functionality for now)

• Lecture 9, Tuesday, December 19 2023 (Web Applications 2):
  Video recording (MP4 Download), Slides, Exercise Sheet 9, Code template for ES9, wikidata-entities-new.tsv (UPDATED), wikidata-entities-new.100k.tsv (UPDATED), wikidata-properties.tsv (400 new triples for predicate counts and labels), Solution for ES9

• Lecture 10, Tuesday, January 16 2024 (Vector Space Model, Word embeddings):
  Video recording (MP4 Download), Slides, Exercise Sheet 10, Code from the lecture, Code template for ES10, movies-plots.tsv, embeddings.fasttext.pt, embeddings.random.pt, Solution for ES10

• Lecture 11, Tuesday, January 23 2024 (Linear Classifiers, Preceptrons, Logistic Regression):
  Video recording (MP4 Download), Slides, Exercise Sheet 11, Code template for ES11, movies-funny.train.tsv, movies-funny.test.tsv, embeddings.fasttext.pt, embeddings.random.pt, Table to enter your results, Solution for ES11

• Lecture 12, Tuesday, January 30 2024 (Language Models):
  Video recording (MP4 Download), Slides, Exercise Sheet 12, Code from the lecture, Code template for ES12, lecture_transcripts.txt