Readings

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:

• Chapters 1-2 in Allemang, Hendler & Gandon (3rd edition)
• Tim Berners-Lee talks about the semantic web
• Slides from the lecture

Useful materials:

• Important knowledge graphs (which we will look more at later):
  • Wikidata (https://www.wikidata.org/)
• 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:

• RDFLib 7.0.0 packages (reference for the labs)
• RDF Grapher for drawing RDF graphs
• RDF Visualizer for drawing RDF graphs
• W3C's RDF 1.1 Concepts and Abstract Syntax
• Pages 25-28, 92-100, 125-128, and 164-167 in Blumauer & Nagy (suggested)

Lecture 3: Querying and updating KGs (SPARQL)

Themes:

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

Mandatory readings (tentative):

• Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
• SPARQL 1.1 Update Language (Sections 1-3)
• rdflib 7.0.0 materials:
  • Querying with SPARQL
• Slides from the lecture

Useful materials:

• SPARQL 1.1 Query Language
• SPARQL 1.1 Update Language (the rest of it)
• SPARQL 1.1 Cheat Sheet
• SPARQL Expressions and Functions
• For example pages 54-55, 133 in Blumauer & Nagy (suggested)
• The Knowledge Graphs for the News example used in the lecture. (Remember to save with the correct .ttl extension.)

Lecture 4: Linked Open Data (LOD)

Themes:

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

Mandatory readings (both lecture 4 and 5):

• Chapter 5 in Allemang, Hendler & Gandon (3rd edition)
• Linked Data, Tim Berners-Lee, 2006-07-27.
• Slides from the lecture

Useful materials

• What Are Linked Data and Linked Open Data?
• Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data-the story so far. Semantic services, interoperability and web applications: emerging concepts, 205-227.

Lecture 5: Open Knowledge Graphs I

Themes:

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

Mandatory readings:

• Chapter 5 in Allemang, Hendler & Gandon (3rd edition)
• Important knowledge graphs - and what to read:
  • Wikidata (https://www.wikidata.org/):
    • Introduction to Wikidata
    • SPARQL query service/A gentle introduction to the Wikidata Query Service
    • example: [1]
  • DBpedia (https://www.dbpedia.org):
    • About Dbpedia
    • example: [2]
• Slides from the lecture

Lecture 6: Open Knowledge Graphs II

Themes:

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

Mandatory readings:

• Chapter 5 in Allemang, Hendler & Gandon (3rd edition)
• Important knowledge graphs - and what to read:
  • GeoNames (https://www.geonames.org/):
    • About GeoNames
    • example: [3]
  • GDELT (https://www.gdeltproject.org/)
    • The GDELT Project - see also the About and Data pages
  • WordNet (https://wordnet.princeton.edu/)
    • WordNet - A lexical database for English
  • BabelNet (https://babelnet.org/):
    • About BabelNet
    • How to use
    • example: [4]
  • ConceptNet (http://conceptnet.io)
    • ConceptNet - An open, multilingual knowledge graph
• Slides from the lecture

Useful materials

• Wikidata statistics
  • Entity statistics
  • Statement statistics
• DBpedia Spotlight
• GDELT documentation
  • Event Codebook (and covers mentions)
  • CAMEO event codes and other codes
  • Global Knowledge Graph Codebook
• Parts 1 and 3 in Blumauer & Nagy's text book (not tightly related to the lecture, but time to finish them by now :-))

Lecture 7: Enterprise Knowledge Graphs

Themes:

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

Mandatory readings:

• Introducing the Knowledge Graph: Things not Strings, Amit Singhal, Google (2012). (The blog post that introduced Google's knowledge graph to the world.)
• A reintroduction to our Knowledge Graph and knowledge panels, Danny Sullivan, Google (2020).
• How Amazon’s Product Graph is helping customers find products more easily, Arun Krishnan, Amazon (2018). (Short blog post that reviews some central ideas from the AutoKnow research paper listed below.)
• Building product graphs automatically, Xin Luna Dong, Amazon (2020).
• JSON for Linking Data
• Slides from the lecture

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:

• Chapters 7-8 in Allemang, Hendler & Gandon (3rd edition)
• Chapter 5 SHACL in Validating RDF (available online)
  • Sections 5.1, 5.3-5.5, and 5.6,1-5.6.3
• W3C's RDF Schema 1.1, focus on sections 1-3 and 6
• Slides from the lecture

Useful materials:

• Interactive, online SHACL Playground
• Lab presentation containing a short overview of SHACL and pySHACL
• pySHACL - A Python validator for SHACL at PyPi.org (after installation, go straight to "Python Module Use".)
• Shapes Constraint Language (SHACL) (Editor's Draft)
• W3C's RDF 1.1 Semantics (the axioms and entailments in sections 8 and 9, are most important, and we will review them in the lecture)
• Inference and Thruth Maintenance in Blazegraph
• OWL-RL adds inference capability on top of RDFLib. To use it, copy the owlrl folder into your project folder, next to your Python files, and import it with import owlrl.
• OWL-RL documentation (most likely more detailed than you will need - check the Python Examples first
• Pages 101-106 in Blumauer & Nagy (suggested)

Lecture 9: Ontologies (OWL)

Themes:

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

Mandatory readings:

• Chapter 9-10, 12-13 in Allemang, Hendler & Gandon (3rd edition)
• OWL2 Primer, sections 2-6 and 9-10
• VOWL: Visual Notation for OWL Ontologies
• Protégé-OWL Getting Started
• Slides from the lecture

Useful materials (cursory):

• OWL 2 Document Overview
• OWL 2 Quick Reference Guide
• OWL2 RDF-Based Semantics
• The OWL-RL materials (from Lecture 5)
• VOWL: Visual Notation for OWL Ontologies
• WebVOWL
• Lohmann et al. (2019): Visualizing Ontologies with VOWL. Semantic Web Journal.
• Pages 106-109 in Blumauer & Nagy (suggested)

Lecture 10: Vocabularies

Themes:

• LOD vocabularies and ontologies

Mandatory readings:

• Chapters 10-11 in Allemang, Hendler & Gandon (3rd edition)
• Linked Open Vocabularies (LOV)
• Important vocabularies / ontologies:
  • Friend of a Friend (FOAF) (if necessary follow the link to the 2004 version)
  • Event Ontology (event)
  • Time ontology in OWL (time, OWL-time)
  • geo: World Geodetic Standard (WGS) 84
  • Dublin Core (DC)
  • SKOS - Simple Knowledge Organization System Home Page
  • Semantic Interlinked Online Communities (SIOC)
  • schema.org - Full Hierarchy
  • DBpedia Ontology
  • Provenance Interchange (PROV)
  • Creative Commons (CC) Vocabulary
  • What we expect you to know about each vocabulary is this:
    • Its purpose and where and how it can be used.
    • Its most central 3-6 classes and properties be able to explain its basic structure.
    • It is less important to get all the names and prefixes 100% right: we do not expect you to learn every little detail by heart.
• Slides from the lecture

Lecture 11: KG embeddings

Themes:

• KG embeddings
• Link prediction
• TorchKGE

Mandatory readings:

• Introduction to Machine Learning for Beginners ( PDF)
• Introduction to Word Embeddings and word2vec ( PDF)
• Introduction to Knowledge Graph Embeddings ( PDF)
• Slides from the lecture

Supplementary readings:

• Mikolov et al’s original word2vec paper
• Bordes et al’s original TransE paper
• Welcome to TorchKGE’ s documentation! (for the labs)

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:

• Slides from the lecture
• No mandatory readings beyond the slides

Supplementary readings:

• Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024). 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). Attention is all you need. Advances in neural information processing systems, 30.
  

 

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