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** ''Cursory'', quickly gets mathematical after the introduction. In particular, sections 2.2.2.3-4 about fixpoint semantics apply to TBoxes with cyclic definitions, which we do not consider in this course. We also do not consider the stuff about rules, epistemics, and reasoning from section 2.2.5 on.
** ''Cursory'', quickly gets mathematical after the introduction. In particular, sections 2.2.2.3-4 about fixpoint semantics apply to TBoxes with cyclic definitions, which we do not consider in this course. We also do not consider the stuff about rules, epistemics, and reasoning from section 2.2.5 on.


==Lecture 11: KG embeddings I==
==Lecture 11: KG embeddings==


Themes:
Themes:
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* [https://torchkge.readthedocs.io/en/latest/ Welcome to TorchKGE’ s documentation!] (for the labs)
* [https://torchkge.readthedocs.io/en/latest/ Welcome to TorchKGE’ s documentation!] (for the labs)


==Lecture 12: Enterprise KGs 2==
==Lecture 12: Enterprise KGs II==


Themes:
Themes:

Revision as of 09:34, 27 March 2023

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 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.

Lecture 1: Introduction to KGs

Themes:

  • Introduction to Knowledge Graphs
  • Organisation of the course

Mandatory readings:

Useful materials:

Lecture 2: Representing KGs (RDF)

Themes:

  • Resource Description Framework (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 (tentative):

Useful materials:

Lecture 4: Open Knowledge Graphs I

Themes:

  • The LOD cloud
  • Important open KGs (LOD datasets)
    • Wikidata
    • DBpedia (lecture 5)
    • GeoNames (lecture 5)
    • the GDELT project (lecture 5)
    • WordNet (lecture 5)
    • BabelNet (lecture 5)
    • ConceptNet (lecture 5)

Mandatory readings (both lecture 4 and 5):

Useful materials

Lecture 5: Open Knowledge Graphs II

See readings for lecture 4.

Lecture 6: Enterprise Knowledge Graphs

Themes:

  • Enterprise Knowledge Graphs (EKGs)
  • Guest lecture with Sindre Asplem from CapGemini, who will talk about CapGemini's experiences with EKGs, including methods and high-level architecture. He will also talk about SHACL, the RDF Shapes Constraint Language (which we may revisit in Lecture 7 and Exercise 7).

Mandatory readings:

  • Slides from Sindre Asplem's guest lecture are available from mitt.uib.no .

Supplementary readings:

  • Parts 2 and 4 in Blumauer & Nagy's text book (strongly suggested - this is where Blumauer & Nagy's book is good!)

Lecture 7: Rules (SHACL and RDFS)

Themes:

  • SHACL and RDFS
  • Axioms, rules and entailment
  • Programming SHACL and 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:

Lecture 10: Formal ontologies (description logic, OWL-DL)

Themes:

  • OWL-DL
  • Description logic
  • Decision problems

Mandatory readings:

Useful materials:

Lecture 11: KG embeddings

Themes:

  • KG embeddings
  • Link prediction
  • TorchKGE

Mandatory readings (preliminary):

Supplementary readings (preliminary):

Lecture 12: Enterprise KGs II

Themes:

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

Mandatory readings:

Supplementary readings:

Lecture 13: Wrapping up

Themes:

  • Knowledge engineering
  • The Ontology Development 101 method

Mandatory readings (preliminary):

Useful materials (preliminary):

  • The rest of Blumauer & Nagy (suggested)


 

INFO216, UiB, 2017-2023, Andreas L. Opdahl (c)