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 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 (preliminary)

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 knowledge Graphs

Themes:

• Introduction to Knowledge Graphs
• Organisation of INFO216

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:

• Pages 27-55 and 105-122 in Blumauer & Nagy (suggested)
• Important knowledge graphs (we will look more at them later!):
  • Wikidata (https://www.wikidata.org/)
  • DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)
  • GeoNames (https://www.geonames.org/)
  • BabelNet (https://babelnet.org/)
  • Linking Open Data (LOD) (http://lod-cloud.net)
  • Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

Lecture 2: Representing KGs (RDF)

Themes:

• RDF
• Programming RDF in Python

Mandatory readings:

• Chapter 3 in Allemang, Hendler & Gandon (3rd edition)
• W3C's RDF 1.1 Primer
• rdflib 6.1.1
  • Main page
  • Getting started with RDFLib
  • Loading and saving RDF
  • Creating RDF triples
  • Navigating Graphs
  • Utilities and convenience functions
• Slides from the lecture

Useful materials:

• Pages 25-28, 92-100, 125-128, and 164-167 in Blumauer & Nagy (suggested)
• W3C's RDF 1.1 Concepts and Abstract Syntax
• RDFLib API documentation (useful reference for the labs)
• RDF Grapher for drawing RDF graphs
• An overview page of some other RDF Data Visualization tools

Lecture 3: Querying and updating KGs (SPARQL)

Themes:

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

Mandatory readings:

• Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
• SPARQL 1.1 Cheat Sheet
• SPARQL 1.1 Update Language (Sections 1-3)
• rdflib 6.1.1 materials:
  • Querying with SPARQL
• Slides from the lecture

Useful materials:

• For example pages 54-55, 133 in Blumauer & Nagy (suggested)
• SPARQL 1.1 Query Language
• SPARQL 1.1 Update Language (the rest of it)
• SPARQL 1.1 Overview
• RDFLib API documentation (same as Session 2)

Lecture 4: Open Knowledge Graphs I

Themes:

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

Mandatory readings:

• Chapter 5 in Allemang, Hendler & Gandon (3rd edition)
• Linked Data, Tim Berners-Lee, 2006-07-27.
• The Linking Open Data (LOD) cloud diagram - The Linked Open Data Cloud
• 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]
  • GeoNames (https://www.geonames.org/):
    • About GeoNames
    • example: [3]
  • GDELT (https://www.gdeltproject.org/)
    • The GDELT Project - see also the About and Data pages
  • EventKG (http://eventkg.l3s.uni-hannover.de/)
    • EventKG - A Multilingual Event-Centric Temporal Knowledge Graph
  • WordNet (https://wordnet.princeton.edu/)
    • WordNet - A lexical database for English
  • BabelNet (https://babelnet.org/):
    • About BabelNet
    • How to use
    • example: [4]
• Slides from the lecture

Useful materials:

• Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data-the story so far. Semantic services, interoperability and web applications: emerging concepts, 205-227.
• Parts 1 and 3 in Blumauer & Nagy's text book (not tightly related to the lecture, but time to finish them by now :-))
• Wikidata statistics
  • Entity statistics
  • Statement statistics
• GDELT documentation
  • Event Codebook (and covers mentions)
  • CAMEO event codes and other codes
  • Global Knowledge Graph Codebook

Lecture 5: Open Knowledge Graphs II

See readings for lecture 4.

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:

• 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.)
• 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!)
• News Angler / News Hunter poster
• AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types. Example of research paper from Amazon - this is a bit heavy for Bachelor level, but you can have a look :-)

Lecture 7: Rules (RDFS)

Themes:

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

Mandatory readings:

• Chapters 7-8 in Allemang, Hendler & Gandon (3rd edition)
• W3C's RDF Schema 1.1, focus on sections 1-3 and 6
• Slides from the lecture

Useful materials:

• Pages 101-106 in Blumauer & Nagy (suggested)
• 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

Lecture 8: Ontologies (OWL)

Themes:

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

Mandatory readings:

• Chapter 9-10 in Allemang, Hendler & Gandon (3rd edition)
• OWL2 Primer, sections 2-6
• VOWL: Visual Notation for OWL Ontologies
• Slides from the lecture (old slides from 2021)

Useful materials (cursory):

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

Lecture 9: Vocabularies

Themes:

• LOD vocabularies and ontologies

Mandatory readings:

• Chapters 10-11 in Allemang, Hendler & Gandon (3rd edition)
• Linked Open Vocabularies (LOV)
• Important vocabularies / ontologies:
  • SKOS - Simple Knowledge Organization System Home Page
  • Dublin Core (DC)
  • Friend of a Friend (FOAF)
  • schema.org - Full Hierarchy
  • DBpedia Ontology
  • Creative Commons (CC) Vocabulary
  • Provenance Interchange (PROV)
  • Event Ontology (event)
  • Time ontology in OWL (time, OWL-time)
  • Semantic Interlinked Online Communities (SIOC)
  • 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 lectures
• Additional slides about the News Angler/News Hunter ontologies (same slides as 2021)

Useful materials:

• More vocabularies / ontologies:
  • geo: World Geodetic Standard (WGS) 84
  • Annotating vocabulary descriptions (VANN)
  • Vocabulary Status (VS)
  • Timeline Ontology (tl)
  • Biographical Information (BIO)
  • Bibliographic Ontology (bibo)
  • Music Ontology (mo)

Lecture 10: Reasoning about KGs (DL)

Themes:

• Description logic
• Decision problems
• OWL-DL

Mandatory readings:

• Slides from the lecture

Useful materials:

• Nardi & Brachman: Introduction to Description Logics. Chapter 1 in Description Logic Handbook.
• Baader & Nutt: Basic Description Logics. Chapter 2 in Description Logic Handbook.
  • 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.
• Complexity of Reasoning in Description Logics. Powered by Evgeny Zolin. (informative)

Lecture 11: Formal ontologies (OWL-DL)

Themes:

• Advanced OWL

Mandatory readings:

• Chapters 12-13 in Allemang, Hendler & Gandon (3rd edition)
• OWL2 Primer
• Slides from the lecture

Useful materials:

• OWL 2 Document Overview
• OWL 2 Quick Reference Guide (cursory)
• VOWL: Visual Notation for OWL Ontologies
• WebVOWL
• Example file demonstrating Protege-OWL reasoning with HermiT.

Owlready2 materials for the lab:

• The section What can I do with Owlready2?
• Welcome to Owlready2's documentation!

Lecture 12: KG embeddings I

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 13: KG embeddings II

See readings for lecture 12.

Lecture 14: Knowledge engineering

Themes:

• Knowledge engineering
• The Ontology Development 101 method

Mandatory readings:

• Chapters 14-16 in Allemang, Hendler & Gandon (3rd edition)
• Noy & McGuinness (2001): Ontology Development 101: A Guide to Creating Your First Ontology.
• Slides from the lecture (old slides from 2021)

Useful materials:

• The rest of Blumauer & Nagy (suggested)

Lecture 14: Wrapping up

 

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