Readings in Knowledge Graphs 2023

Organization: Prof. Dr. Dan Olteanu, Prof. Dr. Dan Suciu (visiting professor)

This seminar explores the current trends in building and using knowledge graphs (KG) from database, machine learning, and logic perspectives. It includes topics such as: query languages for KGs; embeddings of KGs; the logic of graph neural networks; and querying KGs.

Learning outcome: The goal of the seminar is to expose the students to the recent trends in academia and industry on managing knowledge graphs.

Target audience: MSc in Data Science students

Semester: This seminar is offered in Spring 2023.

Teaching format: Each participant: prepares a presentation based on a research paper; answers follow-up technical questions; reads the other papers in the seminar session; and actively participates in the technical discussions in the seminar. Each participant has buddies, who will help improve their presentation by making suggestions for improvements and by attending dry runs of the presentation. The first complete draft of the presentation is due one month after the kickoff meeting. The best presentation of the seminar will be selected by the participants and receive a prize.

Registration: Please register as required by the department. In addition, please browse the papers mentioned below. In an initial meeting, the papers will be assigned to students, so make sure you get assigned to a paper you want.

Meetings:

1. February 20, 2023: The research papers to be discussed in the seminar are overviewed and assigned to the students.
2. February 23, 2023: Invited talk by Prof. Dr. Theo Rekatsinas (ETH and Apple Knowledge Graph) onSAGA, a system developed as part of the knowledge graph effort at Apple.
3. May 6, 2023 from 9am to 6pm: Presentations by the students followed by Q&A.

Participation at all meetings is compulsory. The assessment depends on the quality of the presentation, active participation during the seminar, and input as a buddy.

How to read papers:

• Focus questions to help identify the main contributions of a paper
• Survival kit includes tips on how to read technical sections and the "three-pass approach" to tie all together
• Reading Research Papers by Andrew Ng

How to give talks:

• These two articles have a number of good suggestions.
• This video is pretty good as well.
• How To Speak by Patrick Winston - a newer version of Patrick's talk

1. SPARQL by Examples: Chapter 6 in Aidan Hogan’s book
2. QLever: A Query Engine for Efficient SPARQL+Text Search [Video]
3. Efficient and Effective SPARQL Autocompletion on Very Large Knowledge Graphs

1. Knowledge Graph Embedding: A Survey of Approaches and Applications (Part 1: up to Section 4)
2. Knowledge Graph Embedding: A Survey of Approaches and Applications (Part 2: from Section 4)
3. Representation Learning on Graphs: Methods and Applications

1. Database reasoning over text. [Vision]
2. Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
3. BoxE: A Box Embedding Model for Knowledge Base Completion