Readings

From info216

Textbooks

Main course book (the whole book is mandatory reading):

  • Hogan, A. et al. (2021). Knowledge Graphs. Springer. Synthesis Lectures on Data, Semantics, and Knowledge 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. https://kgbook.org/

Supplementary books (not mandatory):

  • Dean Allemang, James Hendler & Fabien Gandon (2020). Semantic Web for the Working Ontologist, Effective Modeling for Linked Data, RDFS and OWL (Third Edition). ISBN: 9781450376143, PDF ISBN: 9781450376150, Hardcover ISBN: 9781450376174, DOI: 10.1145/3382097.
  • Andreas Blumauer and Helmut Nagy (2020). The Knowledge Graph Cookbook - Recipes that Work. mono/monochrom. ISBN-10: ‎3902796707, ISBN-13: 978-3902796707.

Other materials

In addition, the materials listed below for each lecture are either mandatory or suggested reading. More materials will be added to each lecture in the coming weeks.

The labs, lectures and lectures notes are also part of the curriculum.

Make sure you download the electronic resources to your own computer in good time before the exam. This is your own responsibility. That way you are safe if a site becomes unavailable or somehow damaged the last few days before the exam.

Note: to download some of the papers, you may need to be inside UiB's network. Either use a computer directly on the UiB network or connect to your UiB account through VPN.

Lectures (in progress)

Below are the mandatory and suggested readings for each lecture. All the textbook chapters in Hogan et al. ("Knowledge Graphs") are mandatory, whereas the chapters in Allemang, Hendler & Gandon ("Semantic Web") are suggested.

Session 1: Introduction to KGs

Themes:

  • Introduction to Knowledge Graphs
  • Organisation of the course

Mandatory readings:

Useful materials:

Session 2: Querying and updating KGs (SPARQL)

Themes:

  • SPARQL queries
  • SPARQL Update
  • Programming SPARQL and SPARQL Update in Python

Mandatory readings:

Useful materials:

Session 3: Creating KGs

Themes:

  • Extracting KGs from text
  • Extracting from marked-up sources
  • Extracting from SQL databases and JSON

Mandatory readings:

Useful materials:

Session 4: Validating KGs

Themes:

  • Validating KG schemas (SHACL)
  • Semantic KG schemas/vocabularies (RDFS)

Mandatory readings:

Useful materials:

Session 5: Advanced KGs

Themes:

  • More about RDF, e.g.,
    • identity
    • blank nodes
    • reification
    • higher-arity graphs

Mandatory readings:

  • Sections 3.2 Identity and 3.3 Context in Hogan et al.

Useful materials:

Session 6: Ontologies

Themes:

  • More powerful vocabularies/ontologies (OWL)
  • Creating ontologies

Mandatory readings:

  • Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.

Useful materials:

Session 7: Reasoning

Themes:

  • More about semantic KG schemas (RDFS)
  • Description logic
  • OWL-DL

Mandatory readings:

  • Section 4.2 Rules + DL in Hogan et al.

Useful materials:

Session 8: KG Analytics

Themes:

  • Graph analytics
    • graph metrics
    • directed vector-labelled graphs
    • analysis frameworks and techniques
  • Symbolic learning
    • rule, axiom, and hypothesis mining

Mandatory readings:

  • Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.

Useful materials:

Guest Lecture: KGs in Industry

Guest lecture by Sindre Asplem, Capgemini.

Session 9: KG Embeddings

Themes:

  • Semantic embedding spaces
  • KG embedding techniques
  • Graph neural networks

Mandatory readings:

  • Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.

Useful materials:

  • TorchKGE or PyKeen or ??

Session 10: KG Refinement

Themes:

  • Enriching KGs

Mandatory readings:

  • Chapter 8 Completion + Correction in Hogan et al.

Useful materials:

Session 11: KGs in Practice

Themes:

  • Open KGs
  • Enterprise KGs

Mandatory readings:

Useful materials:



 

INFO216, UiB, 2017-2024, Andreas L. Opdahl (c)
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