Readings: Difference between revisions

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Mandatory readings:
Mandatory readings:
* Sections 3.2 Identity and 3.3 Context in Hogan et al.
* Sections 3.2 Identity and 3.3 Context in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).


Useful materials:
Useful materials:
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Mandatory readings:
Mandatory readings:
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).


Useful materials:
Useful materials:
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Mandatory readings:
Mandatory readings:
* Section 4.2 Rules + DL in Hogan et al.
* Section 4.2 Rules + DL in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).


Useful materials:
Useful materials:
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Mandatory readings:
Mandatory readings:
* Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.
* Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).


Useful materials:
Useful materials:
* [https://networkx.org/ NetworkX - Network analysis in Python]
* [https://networkx.org/ NetworkX - Network analysis in Python]


== Guest Lecture: KGs in Industry ==
==Guest Lecture: KGs in Practice==
Guest lecture by Sindre Asplem, [https://www.capgemini.com/no-no/ Capgemini].
Guest lecture by Sindre Asplem, [https://www.capgemini.com/no-no/ Capgemini].
Mandatory readings:
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).


==Session 9: KG Embeddings==
==Session 9: KG Embeddings==
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Mandatory readings:
Mandatory readings:
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.
* Towards DataScience introduction: [https://towardsdatascience.com/introduction-to-knowledge-graph-embedding-with-dgl-ke-77ace6fb60ef Introduction to Knowledge Graph Embeddings] ([[:file:IntroToKGEmbeddings.pdf | PDF]])
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).
Supplementary readings:
* Towards DataScience introductions:
  * [https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08 Introduction to Machine Learning for Beginners] ([[:file:IntroToMachineLearning.pdf | PDF]])
  * [https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa Introduction to Word Embeddings and word2vec] ([[:file:IntroToWordEmbeddings.pdf | PDF]])
* [[:file:Mikolov_et_al._-_2013_-_Efficient_Estimation_of_Word_Representations_in_Ve.pdf | Mikolov et al’s original word2vec paper]]
* [[:file:Bordes_et_al._-_Translating_Embeddings_for_Modeling_Multi-relation.pdf | Bordes et al’s original TransE paper]]
* [https://torchkge.readthedocs.io/en/latest/ Welcome to TorchKGE’ s documentation!] (for the labs)


Useful materials:
Useful materials:
* TorchKGE or PyKeen or ??
* [https://pykeen.readthedocs.io/en/stable/index.html PyKEEN] or [https://torchkge.readthedocs.io/en/latest/index.html TorchKGE] Python APIs


==Session 10: KG Refinement==
==Session 10: KG Refinement (KGs and LLMs)==


Themes:
Themes:

Revision as of 13:34, 27 March 2025

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:

Useful materials:

Session 6: Ontologies

Themes:

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

Mandatory readings:

Useful materials:

Session 7: Reasoning

Themes:

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

Mandatory readings:

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:

Useful materials:

Guest Lecture: KGs in Practice

Guest lecture by Sindre Asplem, Capgemini.

Mandatory readings:

Session 9: KG Embeddings

Themes:

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

Mandatory readings:

Supplementary readings:

  • Towards DataScience introductions:
 * Introduction to Machine Learning for Beginners ( PDF)
 * Introduction to Word Embeddings and word2vec ( PDF)

Useful materials:

Session 10: KG Refinement (KGs and LLMs)

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:



 

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