Readings: Difference between revisions

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* TorchKGE
* TorchKGE


Mandatory readings (preliminary):
Mandatory readings:
* [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-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]])
* [https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa Introduction to Word Embeddings and word2vec] ([[:file:IntroToWordEmbeddings.pdf | PDF]])
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* [[:file:S11-GraphEmbeddings.pdf | Slides from the lecture]]
* [[:file:S11-GraphEmbeddings.pdf | Slides from the lecture]]


Supplementary readings (preliminary):
Supplementary readings:
* [[:file:Mikolov_et_al._-_2013_-_Efficient_Estimation_of_Word_Representations_in_Ve.pdf | Mikolov et al’s original word2vec paper]]
* [[: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]]
* [[:file:Bordes_et_al._-_Translating_Embeddings_for_Modeling_Multi-relation.pdf | Bordes et al’s original TransE paper]]
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==Lecture 12: KGs and Large Language Models==
==Lecture 12: KGs and Large Language Models==


<!--
Themes:
Themes:
* Questions about the exam
 
* Quizzes
* What are Large Language Models (LLMs)
* Combining KGs and Large Language Models (LLMs)
** retrieval augmented knowledge fusion
** end-to-end KG construction
** LLM-augmented KG to text generation


Mandatory readings:
Mandatory readings:
* The rest of Allemang, Hendler & Gandon (3rd edition)


Useful materials:
* [[:file:S12-KGsAndLLMs.pdf | Slides from the lecture]]
* The rest of Blumauer & Nagy (suggested)
* No mandatory readings beyond the slides
-->
 
Supplementary readings:


* Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024). [[:file:PanEtAl2023-LLMs_KGs_Opportunities_Challenges.pdf | ''Unifying large language models and knowledge graphs: A roadmap.'']]  IEEE Transactions on Knowledge and Data Engineering.
* Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... &  Polosukhin, I. (2017). [[:file:NIPS-2017-attention-is-all-you-need-Paper.pdf | ''Attention is all you need.'']]  Advances in neural information processing systems, 30.<br />


&nbsp;
&nbsp;
<div class="credits" style="text-align: right; direction: ltr; margin-left: 1em;">''INFO216, UiB, 2017-2024, Andreas L. Opdahl (c)''</div>
<div class="credits" style="text-align: right; direction: ltr; margin-left: 1em;">''INFO216, UiB, 2017-2024, Andreas L. Opdahl (c)''</div>

Latest revision as of 14:23, 14 May 2024

Textbooks

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

  • 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.

Supplementary reading book (not mandatory):

  • 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 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 Allemang, Hendler & Gandon are mandatory, whereas the chapters in Blumauer & Nagy are suggested.

Lecture 1: Introduction to KGs

Themes:

  • Introduction to Knowledge Graphs
  • Organisation of the course

Mandatory readings:

Useful materials:

  • Important knowledge graphs (which we will look more at later):
  • Pages 27-55 and 105-122 in Blumauer & Nagy (suggested)

Lecture 2: Representing KGs (RDF)

Themes:

  • Resource Description Framework (RDF)
  • Programming RDF in Python

Mandatory readings:

  • Chapter 3 in Allemang, Hendler & Gandon (3rd edition)
  • W3C's RDF 1.1 Primer until and including 5.1.2 Turtle (but not the rest for now)
  • RDFlib 7.0.0 documentation, the following pages:
    • The main page
    • Getting started with RDFLib
    • Loading and saving RDF
    • Creating RDF triples
    • Navigating Graphs
    • Utilities and convenience functions
    • RDF terms in rdflib
    • Namespaces and Bindings
  • Slides from the lecture

Useful materials:

Lecture 3: Querying and updating KGs (SPARQL)

Themes:

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

Mandatory readings (tentative):

Useful materials:

Lecture 4: Linked Open Data (LOD)

Themes:

  • Linked Open Data(LOD)
  • The LOD cloud
  • Data provisioning

Mandatory readings (both lecture 4 and 5):

Useful materials

Lecture 5: Open Knowledge Graphs I

Themes:

  • Important open KGs (LOD datasets)
    • Wikidata
    • DBpedia

Mandatory readings:

Lecture 6: Open Knowledge Graphs II

Themes:

  • Important open KGs (LOD datasets)
    • DBpedia (continued)
    • GeoNames
    • the GDELT project
    • WordNet
    • BabelNet
    • ConceptNet

Mandatory readings:

Useful materials

Lecture 7: Enterprise Knowledge Graphs

Themes:

  • Enterprise Knowledge Graphs (EKGs)
  • Google’s Knowledge Graph
  • Amazon’s Product Graph
  • JSON-LD (video presentation)

Mandatory readings:

Supplementary readings:

  • Parts 2 and 4 in Blumauer & Nagy's text book (strongly suggested - this is where Blumauer & Nagy's book is good!)
  • LIS: A knowledge graph-based line information system by Grangel-González, I., Rickart, M., Rudolph, O., & Shah, F. (2023, May). In Proceedings of the European Semantic Web Conference (pp. 591-608). Cham: Springer Nature Switzerland.
  • AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types by Dong, X. L., He, X., Kan, A., Li, X., Liang, Y., Ma, J., ... & Han, J. (2020, August). In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2724-2734). Research paper from Amazon about AutoKnow - this is a bit heavy for Bachelor level, but you can have a look :-)

Lecture 8: Rules (SHACL and RDFS)

Themes:

  • SHACL and RDFS
  • Axioms, rules and entailment
  • Programming SHACL and RDFS in Python

Mandatory readings:

Useful materials:

Lecture 9: Ontologies (OWL)

Themes:

  • Basic OWL concepts
  • Axioms, rules and entailments
  • Programming basic OWL in Python

Mandatory readings:

Useful materials (cursory):

Lecture 10: Vocabularies

Themes:

  • LOD vocabularies and ontologies

Mandatory readings:


Lecture 11: KG embeddings

Themes:

  • KG embeddings
  • Link prediction
  • TorchKGE

Mandatory readings:

Supplementary readings:

Lecture 12: KGs and Large Language Models

Themes:

  • What are Large Language Models (LLMs)
  • Combining KGs and Large Language Models (LLMs)
    • retrieval augmented knowledge fusion
    • end-to-end KG construction
    • LLM-augmented KG to text generation

Mandatory readings:

Supplementary readings:

 

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