=Textbooks=

Main course book (''the whole book is mandatory reading''):
* Hogan, A. et al. (2021). '''Knowledge Graphs.''' Springer. ''Synthesis Lectures on Data, Semantics, and Knowledge'' 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. https://kgbook.org/

Supplementary books (''not'' mandatory):
* 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.
* 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 labs, 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 Hogan et al. ("Knowledge Graphs") are mandatory, whereas the chapters in Allemang, Hendler & Gandon ("Semantic Web") are suggested.

==Session 1: Introduction to KGs==

Themes:
* Introduction to Knowledge Graphs
* Organisation of the course

Mandatory readings:
* Chapter 1 Introduction, section 2.1 Models, and Appendix A Background in Hogan et al.
* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]
* [http://rdflib.readthedocs.io/ RDFlib 7.1.3 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
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapters 1-3 in Allemang, Hendler & Gandon (3rd edition)
* Wikidata (https://www.wikidata.org/)

==Session 2: Querying and updating KGs (SPARQL)==

Themes:
* SPARQL queries
* SPARQL Update
* Programming SPARQL and SPARQL Update in Python

Mandatory readings:
* Section 2.2 Queries in Hogan et al.
* [https://graphdb.ontotext.com/documentation/10.8/sparql.html The SPARQL query language — GraphDB 10.8 documentation]
* [https://rdflib.readthedocs.io/ rdflib 7.1.3] materials: [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language]
* [[:File:sparql-1_1-cheat-sheet.pdf | SPARQL 1.1 Cheat Sheet]]
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]

==Session 3: Creating KGs==

Themes:
* Extracting KGs from text
* Extracting from marked-up sources
* Extracting from SQL databases and JSON

Mandatory readings:
* Chapter 6 Creation and Enrichment, sections 6.1-6.4, in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]
* [https://graphdb.ontotext.com/documentation/10.0/virtualization.html Virtualization, GraphDB 10.0 documentation]
* [https://json-ld.org/ JSON for Linking Data]

==Session 4: Validating KGs==

Themes:
* Validating KG schemas (SHACL)
* Semantic KG schemas/vocabularies (RDFS)

Mandatory readings:
* Section 3.1 Schema in Hogan et al.
* Sections 5.1, 5.3, 5.5, 5.6.1, and 5.6.3 in [https://book.validatingrdf.com/bookHtml011.html Gayo, J.E. et al. Validating RDF].
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* SHACL
** Interactive, online [https://shacl.org/playground/ SHACL Playground]
** [https://pypi.org/project/pyshacl/ pySHACL - A Python validator for SHACL at PyPi.org] ''(after installation, go straight to "Python Module Use".)''
** [https://w3c.github.io/data-shapes/shacl/ Shapes Constraint Language (SHACL) (Editor's Draft)]
* RDFS
** [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important, and we will go through the most important ones in the lecture'')
** [https://github.com/RDFLib/OWL-RL 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''.
** [https://owl-rl.readthedocs.io/en/latest/owlrl.html OWL-RL documentation] (most likely more detailed than you will need)

==Session 5: Advanced KGs==

Themes:
* More about RDF, e.g.,
** identity
** blank nodes
** reification
** higher-arity graphs

Mandatory readings:
* Sections 3.2 Identity and 3.3 Context in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]
* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs
* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs

==Session 6: Ontologies==

Themes:
* More powerful vocabularies/ontologies (OWL)
* Creating ontologies

Mandatory readings:
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [http://www.w3.org/TR/owl-primer/ OWL 2 Primer, sections 2-6 (advanced: 9-10)] (show: Turtle)
* [https://service.tib.eu/webvowl/ WebVOWL] interactive OWL visualisation tool
* Selected vocabularies:
** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)
** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]
** [http://dublincore.org/ Dublin Core (DC)]
** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]
** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]
* Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

==Session 7: Reasoning==

Themes:
* More about semantic KG schemas (RDFS)
* Description logic
* OWL-DL

Mandatory readings:
* Section 4.2 Rules + DL in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important'')
* [http://www.w3.org/TR/owl-overview/ W3C OWL 2 Overview]
* [http://www.w3.org/TR/owl-primer/ W3C OWL 2 Primer]
* [https://www.w3.org/TR/2012/REC-owl2-quick-reference-20121211/ W3C OWL 2 Quick Reference Guide (2nd Edition)]

==Session 8: KG Analytics==

Themes:
* Graph analytics
** graph metrics
** directed vector-labelled graphs
** analysis frameworks and techniques
* Symbolic learning
** rule, axiom, and hypothesis mining

Mandatory readings:
* Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://networkx.org/ NetworkX - Network analysis in Python]

==Session 9: KGs in Practice (Guest Lecture)==
Guest lecture by Sindre Asplem, [https://www.capgemini.com/no-no/ Capgemini].

Mandatory readings:
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

==Session 10: KG Embeddings==

Themes:
* Semantic embedding spaces
* KG embedding techniques
* Graph neural networks

Mandatory readings:
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.
** ''In Section 5.2.1, we focus on the Translational Models. The other models are cursory reading.''
* Towards DataScience introduction: [https://towardsdatascience.com/introduction-to-knowledge-graph-embedding-with-dgl-ke-77ace6fb60ef Introduction to Knowledge Graph Embeddings] ([[:file:IntroToKGEmbeddings.pdf | PDF]])
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Supplementary readings:
* Towards DataScience introductions:
** [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]])
* [[: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)

Useful materials:
* [https://pykeen.readthedocs.io/en/stable/index.html PyKEEN] is an alternative Python API. It is similar and may be more up-to-date than TorchKGE.

==Session 11: Graph Neural Networks (GNNs) ==

Themes:
* Graph neural networks
** recurrent/recursive, convolutional, GATs
* Question answering with GNNs (QA-GNN)
* Open KGs:
** WordNet, BabelNet, ConceptNet

Mandatory readings:
* Section 5.3 Graph neural networks in Hogan et al.

Useful materials:
* [[:file:Yasunaga2022-QA-GNN-2104.06378v5.pdf | The QA-GNN paper]]
* [https://conceptnet.io/ ConceptNet:] An open, multilingual knowledge graph
* [https://pytorch-geometric.readthedocs.io/en/latest/ PyG Documentation:] PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

==Session 12: KGs and LLMs==

Themes:
* 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
** KG-LLM synergy

Mandatory readings:
* Chapter 8 Completion + Correction in Hogan et al.

Useful materials:
* [[file:PanEtAl2023-Unifying_Large_Language_Models_and_Knowledge_Graphs_A_Roadmap.pdf | Unifying Large Language Models and Knowledge Graphs: A Roadmap]]
* [[file:Vaswani17-AttentionIsAllYouNeed-1706.03762%281%29.pdf | Attention is All You Need]]
* [[file:HitzlerEtAl-NeuroSymbolicIntegration-swj2291.pdf | Neurosymbolic AI]]

==Session 13: KGs in Practice==

Themes:
* Open KGs
* Enterprise KGs

Mandatory readings:
* Important knowledge graphs:
** Wikidata (https://www.wikidata.org/)
** DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)
** GeoNames (https://www.geonames.org/)
** BabelNet (https://babelnet.org/)
** Linked Open Data (LOD) (http://lod-cloud.net)
** Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

Useful materials:







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

File:PanEtAl2023-Unifying Large Language Models and Knowledge Graphs A Roadmap.pdf

2025-04-28T09:07:59Z

Sinoa:

File:HitzlerEtAl-NeuroSymbolicIntegration-swj2291.pdf

2025-04-28T09:07:25Z

Sinoa:

2025-04-07T08:49:38Z

Sinoa: /* Session 11: Graph Neural Networks (GNNs) */

=Textbooks=

Main course book (''the whole book is mandatory reading''):
* Hogan, A. et al. (2021). '''Knowledge Graphs.''' Springer. ''Synthesis Lectures on Data, Semantics, and Knowledge'' 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. https://kgbook.org/

Supplementary books (''not'' mandatory):
* 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.
* 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 labs, 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 Hogan et al. ("Knowledge Graphs") are mandatory, whereas the chapters in Allemang, Hendler & Gandon ("Semantic Web") are suggested.

==Session 1: Introduction to KGs==

Themes:
* Introduction to Knowledge Graphs
* Organisation of the course

Mandatory readings:
* Chapter 1 Introduction, section 2.1 Models, and Appendix A Background in Hogan et al.
* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]
* [http://rdflib.readthedocs.io/ RDFlib 7.1.3 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
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapters 1-3 in Allemang, Hendler & Gandon (3rd edition)
* Wikidata (https://www.wikidata.org/)

==Session 2: Querying and updating KGs (SPARQL)==

Themes:
* SPARQL queries
* SPARQL Update
* Programming SPARQL and SPARQL Update in Python

Mandatory readings:
* Section 2.2 Queries in Hogan et al.
* [https://graphdb.ontotext.com/documentation/10.8/sparql.html The SPARQL query language — GraphDB 10.8 documentation]
* [https://rdflib.readthedocs.io/ rdflib 7.1.3] materials: [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language]
* [[:File:sparql-1_1-cheat-sheet.pdf | SPARQL 1.1 Cheat Sheet]]
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]

==Session 3: Creating KGs==

Themes:
* Extracting KGs from text
* Extracting from marked-up sources
* Extracting from SQL databases and JSON

Mandatory readings:
* Chapter 6 Creation and Enrichment, sections 6.1-6.4, in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]
* [https://graphdb.ontotext.com/documentation/10.0/virtualization.html Virtualization, GraphDB 10.0 documentation]
* [https://json-ld.org/ JSON for Linking Data]

==Session 4: Validating KGs==

Themes:
* Validating KG schemas (SHACL)
* Semantic KG schemas/vocabularies (RDFS)

Mandatory readings:
* Section 3.1 Schema in Hogan et al.
* Sections 5.1, 5.3, 5.5, 5.6.1, and 5.6.3 in [https://book.validatingrdf.com/bookHtml011.html Gayo, J.E. et al. Validating RDF].
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* SHACL
** Interactive, online [https://shacl.org/playground/ SHACL Playground]
** [https://pypi.org/project/pyshacl/ pySHACL - A Python validator for SHACL at PyPi.org] ''(after installation, go straight to "Python Module Use".)''
** [https://w3c.github.io/data-shapes/shacl/ Shapes Constraint Language (SHACL) (Editor's Draft)]
* RDFS
** [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important, and we will go through the most important ones in the lecture'')
** [https://github.com/RDFLib/OWL-RL 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''.
** [https://owl-rl.readthedocs.io/en/latest/owlrl.html OWL-RL documentation] (most likely more detailed than you will need)

==Session 5: Advanced KGs==

Themes:
* More about RDF, e.g.,
** identity
** blank nodes
** reification
** higher-arity graphs

Mandatory readings:
* Sections 3.2 Identity and 3.3 Context in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]
* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs
* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs

==Session 6: Ontologies==

Themes:
* More powerful vocabularies/ontologies (OWL)
* Creating ontologies

Mandatory readings:
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [http://www.w3.org/TR/owl-primer/ OWL 2 Primer, sections 2-6 (advanced: 9-10)] (show: Turtle)
* [https://service.tib.eu/webvowl/ WebVOWL] interactive OWL visualisation tool
* Selected vocabularies:
** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)
** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]
** [http://dublincore.org/ Dublin Core (DC)]
** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]
** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]
* Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

==Session 7: Reasoning==

Themes:
* More about semantic KG schemas (RDFS)
* Description logic
* OWL-DL

Mandatory readings:
* Section 4.2 Rules + DL in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important'')
* [http://www.w3.org/TR/owl-overview/ W3C OWL 2 Overview]
* [http://www.w3.org/TR/owl-primer/ W3C OWL 2 Primer]
* [https://www.w3.org/TR/2012/REC-owl2-quick-reference-20121211/ W3C OWL 2 Quick Reference Guide (2nd Edition)]

==Session 8: KG Analytics==

Themes:
* Graph analytics
** graph metrics
** directed vector-labelled graphs
** analysis frameworks and techniques
* Symbolic learning
** rule, axiom, and hypothesis mining

Mandatory readings:
* Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://networkx.org/ NetworkX - Network analysis in Python]

==Session 9: KGs in Practice (Guest Lecture)==
Guest lecture by Sindre Asplem, [https://www.capgemini.com/no-no/ Capgemini].

Mandatory readings:
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

==Session 10: KG Embeddings==

Themes:
* Semantic embedding spaces
* KG embedding techniques
* Graph neural networks

Mandatory readings:
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.
** ''In Section 5.2.1, we focus on the Translational Models. The other models are cursory reading.''
* Towards DataScience introduction: [https://towardsdatascience.com/introduction-to-knowledge-graph-embedding-with-dgl-ke-77ace6fb60ef Introduction to Knowledge Graph Embeddings] ([[:file:IntroToKGEmbeddings.pdf | PDF]])
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Supplementary readings:
* Towards DataScience introductions:
** [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]])
* [[: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)

Useful materials:
* [https://pykeen.readthedocs.io/en/stable/index.html PyKEEN] is an alternative Python API. It is similar and may be more up-to-date than TorchKGE.

==Session 11: Graph Neural Networks (GNNs) ==

Themes:
* Graph neural networks
** recurrent/recursive, convolutional, GATs
* Question answering with GNNs (QA-GNN)
* Open KGs:
** WordNet, BabelNet, ConceptNet

Mandatory readings:
* Section 5.3 Graph neural networks in Hogan et al.

Useful materials:
* [:file:Yasunaga2022-QA-GNN-2104.06378v5.pdf The QA-GNN paper]
* [https://conceptnet.io/ ConceptNet:] An open, multilingual knowledge graph
* [https://pytorch-geometric.readthedocs.io/en/latest/ PyG documentation:] PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

==Session 12: KG Refinement (KGs and LLMs)==

Themes:
* Enriching KGs

Mandatory readings:
* Chapter 8 Completion + Correction in Hogan et al.

Useful materials:

==Session 13: KGs in Practice==

Themes:
* Open KGs
* Enterprise KGs

Mandatory readings:
* Important knowledge graphs:
** Wikidata (https://www.wikidata.org/)
** DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)
** GeoNames (https://www.geonames.org/)
** BabelNet (https://babelnet.org/)
** Linked Open Data (LOD) (http://lod-cloud.net)
** Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

Useful materials:







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

2025-03-17T08:17:50Z

Sinoa: /* Lecture: Reasoning */

=Textbooks=

Main course book (''the whole book is mandatory reading''):
* Hogan, A. et al. (2021). '''Knowledge Graphs.''' Springer. ''Synthesis Lectures on Data, Semantics, and Knowledge'' 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. https://kgbook.org/

Supplementary books (''not'' mandatory):
* 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.
* 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 labs, 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 Hogan et al. ("Knowledge Graphs") are mandatory, whereas the chapters in Allemang, Hendler & Gandon ("Semantic Web") are suggested.

==Session 1: Introduction to KGs==

Themes:
* Introduction to Knowledge Graphs
* Organisation of the course

Mandatory readings:
* Chapter 1 Introduction, section 2.1 Models, and Appendix A Background in Hogan et al.
* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]
* [http://rdflib.readthedocs.io/ RDFlib 7.1.3 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
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapters 1-3 in Allemang, Hendler & Gandon (3rd edition)
* Wikidata (https://www.wikidata.org/)

==Session 2: Querying and updating KGs (SPARQL)==

Themes:
* SPARQL queries
* SPARQL Update
* Programming SPARQL and SPARQL Update in Python

Mandatory readings:
* Section 2.2 Queries in Hogan et al.
* [https://graphdb.ontotext.com/documentation/10.8/sparql.html The SPARQL query language — GraphDB 10.8 documentation]
* [https://rdflib.readthedocs.io/ rdflib 7.1.3] materials: [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language]
* [[:File:sparql-1_1-cheat-sheet.pdf | SPARQL 1.1 Cheat Sheet]]
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]

==Session 3: Creating KGs==

Themes:
* Extracting KGs from text
* Extracting from marked-up sources
* Extracting from SQL databases and JSON

Mandatory readings:
* Chapter 6 Creation and Enrichment, sections 6.1-6.4, in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]
* [https://graphdb.ontotext.com/documentation/10.0/virtualization.html Virtualization, GraphDB 10.0 documentation]
* [https://json-ld.org/ JSON for Linking Data]

==Session 4: Validating KGs==

Themes:
* Validating KG schemas (SHACL)
* Semantic KG schemas/vocabularies (RDFS)

Mandatory readings:
* Section 3.1 Schema in Hogan et al.
* Sections 5.1, 5.3, 5.5, 5.6.1, and 5.6.3 in [https://book.validatingrdf.com/bookHtml011.html Gayo, J.E. et al. Validating RDF].
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* SHACL
** Interactive, online [https://shacl.org/playground/ SHACL Playground]
** [https://pypi.org/project/pyshacl/ pySHACL - A Python validator for SHACL at PyPi.org] ''(after installation, go straight to "Python Module Use".)''
** [https://w3c.github.io/data-shapes/shacl/ Shapes Constraint Language (SHACL) (Editor's Draft)]
* RDFS
** [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important, and we will go throught the most important ones in the lecture'')
** [https://github.com/RDFLib/OWL-RL 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''.
** [https://owl-rl.readthedocs.io/en/latest/owlrl.html OWL-RL documentation] (most likely more detailed than you will need)

==Session 5: Advanced KGs==

Themes:
* More about RDF, e.g.,
** identity
** blank nodes
** reification
** higher-arity graphs

Mandatory readings:
* Sections 3.2 Identity and 3.3 Context in Hogan et al.

Useful materials:
* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]
* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs
* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs

==Session 6: Ontologies==

Themes:
* More powerful vocabularies/ontologies (OWL)
* Creating ontologies

Mandatory readings:
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.

Useful materials:
* [http://www.w3.org/TR/owl-primer/ OWL 2 Primer, sections 2-6 (advanced: 9-10)] (show: Turtle)
* [https://service.tib.eu/webvowl/ WebVOWL] interactive OWL visualisation tool
* Selected vocabularies:
** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)
** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]
** [http://dublincore.org/ Dublin Core (DC)]
** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]
** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]
* Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

==Session 7: Reasoning==

Themes:
* More about semantic KG schemas (RDFS)
* Description logic
* OWL-DL

Mandatory readings:
* Section 4.2 Rules + DL in Hogan et al.

Useful materials:
* RDFS
* OWL-DL

==Session 8: KG Analytics==

Themes:
* Graph analytics
** graph metrics
** directed vector-labelled graphs
** analysis frameworks and techniques
* Symbolic learning
** rule, axiom, and hypothesis mining

Mandatory readings:
* Sections 5.1 Graph Analytics and 5.4 Symbolic Learning in Hogan et al.

Useful materials:
* [https://networkx.org/ NetworkX - Network analysis in Python]

== Guest Lecture: KGs in Industry ==
Guest lecture by Sindre Asplem, [https://www.capgemini.com/no-no/ Capgemini].

==Session 9: KG Embeddings==

Themes:
* Semantic embedding spaces
* KG embedding techniques
* Graph neural networks

Mandatory readings:
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.

Useful materials:
* TorchKGE or PyKeen or ??

==Lecture: KG Refinement==

Themes:
* Enriching KGs

Mandatory readings:
* Chapter 8 Completion + Correction in Hogan et al.

Useful materials:

==Lecture: KGs in Practice==

Themes:
* Open KGs
* Enterprise KGs

Mandatory readings:
* Important knowledge graphs:
** Wikidata (https://www.wikidata.org/)
** DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)
** GeoNames (https://www.geonames.org/)
** BabelNet (https://babelnet.org/)
** Linked Open Data (LOD) (http://lod-cloud.net)
** Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

Useful materials:

==Lecture: KG Quality==

Themes:
* KG completion and correction
* Best practices
* Access protocols and usage control

Mandatory readings:
* Chapters 8 Completion + Correction and 9 Best Practices + Access Protocols + Usage Control in Hogan et al.

Useful materials:

==Lecture: KGs in industry==

I am also working to organise a Guest Lecture from a practitioner and former student.





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

Readings

2025-03-03T09:42:38Z

Sinoa:

=Textbooks=

Main course book (''the whole book is mandatory reading''):
* Hogan, A. et al. (2021). '''Knowledge Graphs.''' Springer. ''Synthesis Lectures on Data, Semantics, and Knowledge'' 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer. https://kgbook.org/

Supplementary books (''not'' mandatory):
* 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.
* 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 labs, 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 Hogan et al. ("Knowledge Graphs") are mandatory, whereas the chapters in Allemang, Hendler & Gandon ("Semantic Web") are suggested.

==Session 1: Introduction to KGs==

Themes:
* Introduction to Knowledge Graphs
* Organisation of the course

Mandatory readings:
* Chapter 1 Introduction, section 2.1 Models, and Appendix A Background in Hogan et al.
* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]
* [http://rdflib.readthedocs.io/ RDFlib 7.1.3 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
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapters 1-3 in Allemang, Hendler & Gandon (3rd edition)
* Wikidata (https://www.wikidata.org/)

==Session 2: Querying and updating KGs (SPARQL)==

Themes:
* SPARQL queries
* SPARQL Update
* Programming SPARQL and SPARQL Update in Python

Mandatory readings:
* Section 2.2 Queries in Hogan et al.
* [https://graphdb.ontotext.com/documentation/10.8/sparql.html The SPARQL query language — GraphDB 10.8 documentation]
* [https://rdflib.readthedocs.io/ rdflib 7.1.3] materials: [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* Chapter 6 in Allemang, Hendler & Gandon (3rd edition)
* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language]
* [[:File:sparql-1_1-cheat-sheet.pdf | SPARQL 1.1 Cheat Sheet]]
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]

==Session 3: Creating KGs==

Themes:
* Extracting KGs from text
* Extracting from marked-up sources
* Extracting from SQL databases and JSON

Mandatory readings:
* Chapter 6 Creation and Enrichment, sections 6.1-6.4, in Hogan et al.
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]
* [https://graphdb.ontotext.com/documentation/10.0/virtualization.html Virtualization, GraphDB 10.0 documentation]
* [https://json-ld.org/ JSON for Linking Data]

==Session 4: Validating KGs==

Themes:
* Validating KG schemas (SHACL)
* Semantic KG schemas/vocabularies (RDFS)

Mandatory readings:
* Section 3.1 Schema in Hogan et al.
* Sections 5.1, 5.3, 5.5, 5.6.1, and 5.6.3 in [https://book.validatingrdf.com/bookHtml011.html Gayo, J.E. et al. Validating RDF].
* The slides from the lecture (available under [https://mitt.uib.no/courses/51914/files/folder/Slides Files/Slides in http://mitt.uib.no]).

Useful materials:
* SHACL
** Interactive, online [https://shacl.org/playground/ SHACL Playground]
** [https://pypi.org/project/pyshacl/ pySHACL - A Python validator for SHACL at PyPi.org] ''(after installation, go straight to "Python Module Use".)''
** [https://w3c.github.io/data-shapes/shacl/ Shapes Constraint Language (SHACL) (Editor's Draft)]
* RDFS
** [https://www.w3.org/TR/rdf11-mt/ W3C's RDF 1.1 Semantics] (''the axioms and entailments in sections 8 and 9 are most important, and we will go throught the most important ones in the lecture'')
** [https://github.com/RDFLib/OWL-RL 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''.
** [https://owl-rl.readthedocs.io/en/latest/owlrl.html OWL-RL documentation] (most likely more detailed than you will need)

==Session 5: Advanced KGs==

Themes:
* More about RDF, e.g.,
** identity
** blank nodes
** reification
** higher-arity graphs

Mandatory readings:
* Sections 3.2 Identity and 3.3 Context in Hogan et al.

Useful materials:
* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]
* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs
* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs

==Session 6: Ontologies==

Themes:
* More powerful vocabularies/ontologies (OWL)
* Creating ontologies

Mandatory readings:
* Sections 4.1 Ontologies and 6.3 Schema/ontology creation in Hogan et al.

Useful materials:
* [http://www.w3.org/TR/owl-primer/ OWL 2 Primer, sections 2-6 (advanced: 9-10)] (show: Turtle)
* [https://service.tib.eu/webvowl/ WebVOWL] interactive OWL visualisation tool
* Selected vocabularies:
** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)
** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]
** [http://dublincore.org/ Dublin Core (DC)]
** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]
** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]
* Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

==Lecture: Reasoning==

Themes:
* More about semantic KG schemas (RDFS)
* Description logic
* OWL-DL

Mandatory readings:
* Section 4.2 Rules + DL in Hogan et al.

Useful materials:
* RDFS
* OWL-DL

==Lecture: KG Analytics==

Themes:
* Graph analytics

Mandatory readings:
* Section 5.1 Graph Analytics in Hogan et al.

Useful materials:
* networkx

==Lecture: KG Embeddings==

Themes:
* Semantic embedding spaces
* KG embedding techniques
* Graph neural networks

Mandatory readings:
* Sections 5.2 Knowledge Graph Embeddings and 5.3 Graph neural networks in Hogan et al.

Useful materials:
* TorchKGE or PyKeen or ??

==Lecture: KG Refinement==

Themes:
* Enriching KGs

Mandatory readings:
* Chapter 8 Completion + Correction in Hogan et al.

Useful materials:

==Lecture: KGs in Practice==

Themes:
* Open KGs
* Enterprise KGs

Mandatory readings:
* Important knowledge graphs:
** Wikidata (https://www.wikidata.org/)
** DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)
** GeoNames (https://www.geonames.org/)
** BabelNet (https://babelnet.org/)
** Linked Open Data (LOD) (http://lod-cloud.net)
** Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)

Useful materials:

==Lecture: KG Quality==

Themes:
* KG completion and correction
* Best practices
* Access protocols and usage control

Mandatory readings:
* Chapters 8 Completion + Correction and 9 Best Practices + Access Protocols + Usage Control in Hogan et al.

Useful materials:

==Lecture: KGs in industry==

I am also working to organise a Guest Lecture from a practitioner and former student.





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

MediaWiki:Sidebar

2025-03-03T09:15:36Z

Sinoa:

Exams

2025-03-03T09:13:40Z

Sinoa:

Here are all the past exams in INFO216. ''Note that the continuation exams (in the autumn) usually are different from the ordinary (spring) exams: there is less multiple choice because there are fewer students...''

* [[:File:skeks-ord-2024-m-korrekte-svar.pdf | Spring 2024]]
** due to a strike in public sector, this exam text was not created by the professor who taught the course: therefore, the "question style" in the exams for 2022 and 2023 are better examples of what you can expect for the ordinary 2025 exam
** also, the curriculum has changed since 2024
** [solution examples and comments] to the exam will follow
* [[:File:skeks-ord-2023-m-korrekte-svar.pdf | Spring 2023]]
** the little green hooks on pages 1-25 show correct answers to the closed questions in Task 1
** [[solution examples 2023 | examples related to the open questions from page 26 onward]]

* [[:File:skeks-ord-2022-m-korrekte-svar.pdf | Spring 2022]]
** the little green hooks show correct answers to the closed questions in tasks 1, 3 and 5
** [[solution examples 2022 | examples related to the open questions]]
** the program file mentioned in the last task about RDFlib errors is available [[:File:Question_78_115275701_1653650433860.pdf | here]]
** see further comments below
* [[:File:skeks-ord-2021-m-korrekte-svar.pdf | Spring 2021]]
** the little green hooks show correct answers to the closed questions
** [[solution examples 2021 | examples related to the open questions]] (77-88 and 94-99)
** see further comments below
* [[:File:INFO216-exam-2020-spring.pdf | Spring 2020]]
* [[:File:INFO216-exam-2019.pdf | Spring 2019]]
* [[:File:INFO216-exam-2018-spring.pdf | Spring 2018]]
* [[:File:INFO216-spring2017.pdf | Spring 2017]]
* [[:File:INFO216-autumn2016.pdf | Autumn 2016]]
* [[:File:INFO216-spring2016.pdf | Spring 2016]]
* [[:File:INFO216-spring2015.pdf | Spring 2015]]
* [[:File:INFO216-spring2014.pdf | Spring 2014]]



'''About the Spring 2022 exam:'''
* There is a problem with question 22: The answer states that ''hasSibling'' (excluding half-siblings) is Symmetric and Transitive, and also Irreflexive. But, if there is at least one pair of siblings, symmetry+transitivity implies reflexivity, so it cannot in practice be Irreflexive too. When there are errors like this in the questions, we always grade to your advantage as students: so that both the anticipated and correct answers are given full score.
* On Task 3, BIBO is no longer in the curriculum.

'''About the Spring 2021 exam:'''
* On Task 2, these vocabularies are no longer in the curriculum: BIBO, BIO, MO, VS, VANN
* On Task 3, this open KG is no longer in the curriculum: EventKG
* The questions about OWL properties were open to interpretations so there may be more ok answers than indicated.
* Additional information given during/after the exam:
** Unfortunately, there is an error in the first question in part 3 of the INFO216 exam: "Which open knowledge graph (or knowledge base) is best matched?"
*** "Contains information about more than 90 billion things." This should have said "millions", not "billions", so we will ignore this question during the correction ... Sorry about that!
** On the last question in part 5, we will of course accept both answers with "city population" and with "city count":
*** "Continue with the same triple store. Extend the previous SPARQL query so that it lists the city population in each region in Norway in descending order."

 
<div class="credits" style="text-align: right; direction: ltr; margin-left: 1em;">''INFO216, UiB, 2017-2020, Andreas L. Opdahl (c)''</div>

2025-02-25T18:09:23Z

Sinoa: /* If you really have a lot of time */

==Topics==
* Creating SQLite3 databases
* Creating OBDA or R2RML mappings
* Setting up Ontop in GraphDB
* Querying SQLite3 databases through a virtual Ontop KG

==Useful materials==
* The [https://sqlite.org/index.html SQLite] project page.
* [https://graphdb.ontotext.com/documentation/10.8/virtualization.html Accessing relational databases with data virtualization] (GraphDB's Ontop documentation)
* The [https://github.com/ontop/ontop-examples/tree/master ontop-examples] repository on GitHub contains many OBDA mapping examples
* Section 2 in [https://www.w3.org/TR/sparql11-federated-query/ SPARQL 1.1 Federated Query]

==Tasks==
We recommend you run this exercise through the Ontop plugin to the free desktop version of OntoText's GraphDB tool. You can also use one of the [https://ontop-vkg.org/ open-source versions of Ontop].

===Installing and running SQLite3===
'''Task:''' Download and install a relational database system on your computer. If you are not experienced in databases, we recommend the very light-weight and easy-to-install [https://sqlite.org/download.html SQLite3].

(Unfortunately, SQLite3 is not fully integrated with Ontop. A more powerful alternative is H2. At the end of this exercise you will find guidelines for doing the exercise with H2 instead.)

'''Task:''' Create a folder on your local computer for this exercise. Go to the new folder and open a Console or Terminal window. Start SQLite3 with the name of a new database, for example 'MyTestDB.sqlite'.

'''Task:''' Based on the example files ''Fig6.3-ReportClaimantTable-original.sql'' in [https://mitt.uib.no/courses/51914/files/folder/Examples the Examples folder in mitt.uib.no], create the following two tables and fill them with test data:

Table '''InvestigatedPeople:'''
{| class="wikitable"
|'''InvestigationID'''
|'''American'''
|'''CPDate'''
|'''CPDays'''
|'''IndictmentDays'''
|'''Investigation'''
|'''Name'''
|'''Outcome'''
|'''Overturned'''
|'''Pardoned'''
|-
|investigation_114
|true
|None
|None
|None
|bush-clinton-passport
|None
|None
|false
|false
|-
|investigation_100
|true
|1993-10-26
|1335
|789
|pierce
|Deborah Gore Dean
|conviction
|false
|false
|-
|investigation_113
|true
|None
|None
|None
|sealed-investigation-hwbush-2
|None
|None
|false
|false
|-
|investigation_10
|true
|1975-01-01
|592
|286
|watergate
|Robert C. Mardian
|conviction
|true
|false
|-
|investigation_11
|true
|None
|None
|<nowiki>-9</nowiki>
|watergate
|Maurice H. Stans
|indictment
|false
|false
|-
|investigation_115
|true
|1994-03-22
|<nowiki>-136</nowiki>
|<nowiki>-316</nowiki>
|whitewater
|David L. Hale
|guilty-plea
|false
|false
|}
Table '''Investigations:'''
{| class="wikitable"
|'''Investigation'''
|'''InvestigationDays'''
|'''InvestigationStart'''
|'''InvestigationEnd'''
|'''President'''
|-
|bush-clinton-passport
|1081
|1992-12-14
|1995-11-30
|Bill Clinton
|-
|pierce
|3162
|1990-03-01
|1998-10-27
|George H.W. Bush
|-
|sealed-investigation-hwbush-2
|453
|1991-04-19
|1992-07-15
|George H.W. Bush
|-
|watergate
|1492
|1973-05-19
|1977-06-19
|Richard Nixon
|-
|whitewater
|2770
|1994-08-05
|2002-03-06
|Bill Clinton
|}

''Tip:'' You can either enter SQL statements interactively one by one, or you can save them to a file and use SQLite3's ''.read'' command. Use the ''.exit'' command when you are done.

''Tip:'' Although SQLite3 does not require it, Ontop will later expect that you have declared all PRIMARY KEYS as NOT NULL (for example: "InvestigationID text NOT NULL PRIMARY KEY,").

(If you want more data, the full CSV file is [https://github.com/fivethirtyeight/data/blob/master/russia-investigation/russia-investigation.csv available on GitHub].)

===OBDA and R2RML mappings===
'''Task:''' Draw an example graph on paper that represents the ''investigation_100'' and ''pierce'' rows of the two tables.

'''Task:''' Based on the example files ''Fig6.4-ReportClaimant-mapping.obda'' and/or ''Fig6.4-ReportClaimant-mapping.r2rml'' in [https://mitt.uib.no/courses/51914/files/folder/Examples the Examples folder], create a mapping from the two tables into an RDF graph.

===GraphDB Ontop configuration===
'''Task:''' You already have Ontop as part of your GraphDB installation, but you need an additional ''.jar''-file - a JDBC driver - for connecting to your database of choice. For SQLite3, you can download the driver from [https://github.com/xerial/sqlite-jdbc Xenial on GitHub]. Place the ''.jar''-file in the ''lib/''-subfolder of your GraphDB installation folder(*). Restart Ontop.

(*) On Linux, the path is <ONTOP_INSTALL_DIR>/lib/app/lib/sqlite-jdbc-3.49.0.0.jar .

'''Task:'''
* Create a new Ontop repository.
* The ''Driver class'' should be <code>org.sqlite.JDBC</code>. The ''JDBC URL'' should be <code>jdbc:sqlite:PATH</code>, where PATH is the location of your SQLite3 database, for example <code>jdbc:sqlite:C:/Users/MyUser/OntopLab/MyTestDB.sqlite</code>.
* Load the OBDA or R2RML mapping file.
* Click ''Create'' and activate the new repository.

===Running SPARQL queries===
'''Task:''' Write SPARQL queries that answer the following questions:
* List all investigations in your graph.
* Count the number of investigations in your graph.
* List all investigated people in your graph.
* List all investigated people in your graph along with the investigations they were involved in.
* You can also re-do the following queries from before (you may need to load more data):
** List everyone who pleaded guilty, along with the name of the investigation.
** List everyone who were convicted, but who had their conviction overturned by which president.
** For each investigation, list the number of indictments made.
** For each investigation with multiple indictments, list the number of indictments made.
** For each investigation with multiple indictments, list the number of indictments made, sorted with the most indictments first.

==If you have more time==
'''Task:''' Create a new ''No inference'' (plain RDF) GraphDB repository and import triples from the file ''Presidents.ttl'' in [https://mitt.uib.no/courses/51914/files/folder/Examples the Examples folder in mitt.uib.no].

Activate the new repository. Its triples connect the names of recent US presidents to their Wikidata URIs. Run a SPARQL SELECT query to list them all.

'''Task:''' Create a new ''Federated'' (FedX Virtual SPARQL) repository.
* As a local repository, add the new ''Presidents'' repository.
* As a remote repository, add ''https://query.wikidata.org/sparql'' as a ''Generic SPARQL endpoint''.
Activate the new repository.

'''Task:''' Re-run the SPARQL SELECT query that lists the names of recent US presidents along with their Wikidata URIs.

'''Task:''' Extend the SPARQL SELECT query to also list the ''schema:description'' of each US president.

'''Task:''' Use ''FILTER'' and the ''LANG'' function to only list president descriptions with language ''"en"''.

''...from here we could have gone on to add the Ontop repository too as a local repository, but Ontop does not support SQLite3 fully, unfortunately. Using H2 instead of SQLite3 is an option, as we outline below.''

''Tip:'' Here are some useful PREFIXES:

PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX schema: <http://schema.org/>

==If you ''really'' have a lot of time==
It is possible to create a ''Federated'' (FedX Virtual SPARQL) repository that federates
* a local repository (like the ''Presidents'' repository),
* a remote repository (for example ''https://query.wikidata.org/sparql'' as a ''Generic SPARQL endpoint''), ''and''
* a virtual repository (like the ''Investigations'' and ''InvestigatedPeople'' repository) as long as it runs on top of ''H2'' instead of ''SQLite3''.

'''Task:''' Install a recent Java on your computer if you do not have already, for example [https://adoptium.net/en-GB/download/ the open version from Adoptium].
java -version
must show version 11 or later, and Java cannot be blocked by your firewall.

'''Task:''' [https://www.h2database.com/html/download.html Download] and [https://www.h2database.com/html/installation.html install H2].

Again, Ontop needs an additional ''.jar''-file - a JDBC driver - for connecting to your H2 database. You will find this file in the folder where you installed H2. It is called something like ''h2-2.3.232.jar''. Place a copy of it in ''lib/''-subfolder of your GraphDB installation folder (same place where you put the ''sqlite-jdbc-3.49.0.0.jar''-file). Restart Ontop.

'''Task:''' Start H2. Normally it opens a browser window on port ''8082'', but there is a console (command line) option too.
* Default driver class: ''org.h2.Driver'' - this one is fine
* Default JDBC URL: ''jdbc:h2:~/test'' - if you want, you can replace the ''~'' with a full path and perhaps give the DB a better name than ''test'' too
You do not need a password or to change the user name.

'''Task:''' Click connect and define your database. You can cut-and-paste the same SQL commands as before to create the ''Investigations'' and ''InvestigatedPeople'' databases. But be aware of a few critical differences:
* H2 uses ''schemas'' to organise tables. So first you need to create one for your two tables, for example:
CREATE SCHEMA MyTestSchema ;
* After that, you must always prefix your table names with the schema: ''MyTestSchema.Investigations'' and ''MyTestSchema.InvestigatedPeople'' - both in your SQL commands and in your OBDA mappings.
* If you do not write schema, table, and column names inside "quotation marks", H2 will automatically convert them to UPPER CASE.

'''Task:''' Create a new virtual repository like you did before, but using the driver class and JDBC URL for H2 instead. You need to enter a H2 user name too. This virtual repository is more stable than the one that wraps SQLite3.

You may need to disconnect from H2 in the browser window before you connect to it via GraphDB and Ontop.

'''Task:''' Add the new virtual repository to the FedX federated repository you created before. You can now write a SPARQL query that combines triples from all the three repositories it contains, e.g.,

* For all investigations, list its name, who was president, that president's Wikidata URL, and a brief description of the president in English.

Lab: Ontop

2025-02-25T18:07:53Z

Sinoa: /* If you have more time */