Readings: Difference between revisions

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Mandatory readings:
Mandatory readings:
* [[:File:S10-DescriptionLogic.pdf]]
* [[:File:S10-DescriptionLogic.pdf | Slides from the lecture]]


Useful materials:
Useful materials:
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* Chapters 12-13 in Allemang, Hendler & Gandon (3rd edition)
* Chapters 12-13 in Allemang, Hendler & Gandon (3rd edition)
* [http://www.w3.org/TR/owl-primer OWL2 Primer]
* [http://www.w3.org/TR/owl-primer OWL2 Primer]
* [[:File:S1|-OWL-DL.pdf | Slides from the lecture]]
* [[:File:S11-OWL-DL.pdf | Slides from the lecture]]


Useful materials:
Useful materials:
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* [http://vowl.visualdataweb.org/webvowl/index.html#sioc WebVOWL]
* [http://vowl.visualdataweb.org/webvowl/index.html#sioc WebVOWL]
* [[:File:DL-reasoning-RoyalFamily-final.owl.txt | Example file]] demonstrating Protege-OWL reasoning with HermiT.
* [[:File:DL-reasoning-RoyalFamily-final.owl.txt | Example file]] demonstrating Protege-OWL reasoning with HermiT.
Owlready2 materials for the lab:
* The section [https://pypi.org/project/Owlready2/ What can I do with Owlready2?]
* [https://owlready2.readthedocs.io/en/latest/ Welcome to Owlready2's documentation!]


==Lecture 12: KG embeddings==
==Lecture 12: KG embeddings==
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Mandatory readings:
Mandatory readings:
* [https://torchkge.readthedocs.io/en/latest/ TorchKGE]
* [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-knowledge-graph-embedding-with-dgl-ke-77ace6fb60ef Introduction to Knowledge Graph Embeddings] ([[:file:IntroToKGEmbeddings.pdf | PDF]])
* [[:file:S12-GraphEmbeddings.pdf | Slides from the lecture]]


Supplementary readings:
* [[: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)


==Lecture 13: Knowledge Engineering==
==Lecture 13: Knowledge Engineering==

Revision as of 11:39, 24 April 2022

Textbooks

Main course book:

  • 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. The whole book is mandatory reading.

Supplementary text 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

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.

To be updated - the readings for the later lectures are not yet final for Spring 2022.


Lecture 1: Introduction to knowledge Graphs

Themes:

  • Introduction to Knowledge Graphs
  • Organisation of INFO216

Mandatory readings:

Useful materials:


Lecture 2: Representing KGs (RDF)

Themes:

  • RDF
  • Programming RDF in Python

Mandatory readings:

Useful materials:


Lecture 3: Querying and updating KGs (SPARQL)

Themes:

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

Mandatory readings:

Useful materials:


Lecture 4: Storing and sharing KGs

Themes:

  • Triple stores and Blazegraph
  • Web APIs and JSON-LD
  • Other serialisation formats

Mandatory readings:

Useful materials:


Lecture 5: Open Knowledge Graphs

Themes:

  • The LOD cloud
  • Important open KGs (LOD datasets)
    • Wikidata
    • DBpedia
    • the GDELT project
    • EventKG
    • GeoNames
    • WordNet
    • BabelNet
    • and others

Mandatory readings:

Useful materials:


Lecture 6: Enterprise Knowledge Graphs

Themes:

  • Enterprise Knowledge Graphs
  • Google’s Knowledge Graph
  • Amazon’s Product Graphs
  • News Hunter’s infrastructure and architecture

Mandatory readings:

Supplementary readings:


Lecture 7: Rules (RDFS)

Themes:

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

Mandatory readings:

Useful materials:


Lecture 8: Ontologies (OWL)

Themes:

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

Mandatory readings:

Useful materials (cursory):


Lecture 9: Vocabularies

Themes:

  • LOD vocabularies and ontologies

Mandatory readings:

Useful materials:

Lecture 10: Reasoning about KGs (DL)

Themes:

  • Description logic
  • Decision problems
  • OWL-DL

Mandatory readings:

Useful materials:

Lecture 11: Formal ontologies (OWL-DL)

Themes:

  • Advanced OWL

Mandatory readings:

Useful materials:

Owlready2 materials for the lab:

Lecture 12: KG embeddings

Themes:

  • KG embeddings
  • Link prediction
  • TorchKGE

Mandatory readings:

Supplementary readings:

Lecture 13: Knowledge Engineering

Themes:

  • Knowledge engineering
  • The Ontology Development 101 method

Mandatory readings:

Useful materials:

  • The rest of Blumauer & Nagy (suggested)


Lecture 14: Wrapping up

 

INFO216, UiB, 2017-2022, Andreas L. Opdahl (c)
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