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=Text book=
 
The text book in INFO216 is ''Semantic Web for the Working Ontologist, Second Edition: Effective Modeling in RDFS and OWL by Dean Allemang and James Hendler (Jun 3, 2011). Morgan Kaufmann.'' '''The whole book is obligatory reading.'''  
=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=
=Other materials=
In addition, '''the materials listed below for each lecture is either mandatory or suggested reading.''' Make sure you download the papers and web sites in good time before the exam. That way you are safe if a site becomes unavailable or somehow damaged the last few days before the exam. Note that to download some of the papers, you need to be inside UiB's network. Either use a computer directly on the UiB network or connect to your UiB account with VPN if you are elsewhere.


Finally, '''the lectures and lectures notes are also in the curriculum.'''
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)
* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]
* [[:File:S01-KnowledgeGraphs.pdf | Slides from the lecture]]
 
Useful materials:
* Important knowledge graphs (''which we will look more at 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/)
-->
* 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)
* [https://www.w3.org/TR/rdf11-primer/ W3C's RDF 1.1 Primer] until and including 5.1.2 Turtle (but not the rest for now)
* [http://rdflib.readthedocs.io/ 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
* [[:File:S02-RDF.pdf | Slides from the lecture]]
 
Useful materials:
* [https://rdflib.readthedocs.io/en/stable/apidocs/modules.html RDFLib 7.0.0 packages] (reference for the labs)
* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs
* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs
* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]
<!-- * An overview page of some other [https://www.w3.org/2018/09/rdf-data-viz/ RDF Data Visualization tools] -->
* 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)
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language] (Sections 1-3)
* [https://rdflib.readthedocs.io/ rdflib 7.0.0] materials:
** [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]
* [[:File:S03-SPARQL.pdf | Slides from the lecture]]
 
Useful materials:
<!-- * [https://medium.com/wallscope/constructing-sparql-queries-ca63b8b9ac02 Constructing SPARQL Queries] -->
* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]
* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language] (the rest of it)
* [[: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]
* For example pages 54-55, 133 in Blumauer & Nagy (suggested)
* The [[:File:kg4news-dump-20230130.txt | 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)
* [https://www.w3.org/DesignIssues/LinkedData.html Linked Data], Tim Berners-Lee, 2006-07-27.
* [[:File:S04-LOD.pdf | Slides from the lecture]]
 
Useful materials
* [https://www.ontotext.com/knowledgehub/fundamentals/linked-data-linked-open-data/ What Are Linked Data and Linked Open Data?]
* [[:File:BizerHeathBernersLee-LinkedData2009-TheStorySoFar.pdf | 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/):
*** [https://www.wikidata.org/wiki/Wikidata:Introduction Introduction to Wikidata]
*** [https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/Wikidata_Query_Help SPARQL query service/A gentle introduction to the Wikidata Query Service]
*** example: [https://www.wikidata.org/wiki/Q26793]
** DBpedia (https://www.dbpedia.org):
*** [http://wiki.dbpedia.org/about About Dbpedia]
*** example: [https://dbpedia.org/resource/Bergen]
*  [[:File:S05-S06-OpenKGs.pdf | 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/):
*** [http://www.geonames.org/about.html About GeoNames]
*** example: [https://www.geonames.org/3161732/bergen.html]
** GDELT (https://www.gdeltproject.org/)
*** [https://www.gdeltproject.org/ The GDELT Project] - see also the About and Data pages
** WordNet (https://wordnet.princeton.edu/)
*** [https://wordnet.princeton.edu/ WordNet - A lexical database for English]
** BabelNet (https://babelnet.org/):
*** [http://live.babelnet.org/about About BabelNet]
*** [https://babelnet.org/how-to-use How to use]
*** example: [https://babelnet.org/synset?id=bn%3A00010008n&orig=Bergen&lang=EN]
** ConceptNet (http://conceptnet.io)
*** [http://conceptnet.io ConceptNet - An open, multilingual knowledge graph]
*  [[:File:S05-S06-OpenKGs.pdf | Slides from the lecture]]
 
Useful materials
* Wikidata statistics
** [https://grafana.wikimedia.org/d/000000167/wikidata-datamodel?orgId=1&refresh=30m Entity statistics]
** [https://grafana.wikimedia.org/d/000000175/wikidata-datamodel-statements?orgId=1&refresh=30m Statement statistics]
* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]
* GDELT documentation
** [http://data.gdeltproject.org/documentation/GDELT-Event_Codebook-V2.0.pdf Event Codebook (and covers mentions)]
** [http://data.gdeltproject.org/documentation/CAMEO.Manual.1.1b3.pdf CAMEO event codes and other codes]
** [http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook-V2.1.pdf 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:
* [https://www.blog.google/products/search/introducing-knowledge-graph-things-not/ Introducing the Knowledge Graph: Things not Strings], Amit Singhal, Google (2012). ''(The blog post that introduced Google's knowledge graph to the world.)''
* [https://blog.google/products/search/about-knowledge-graph-and-knowledge-panels/ A reintroduction to our Knowledge Graph and knowledge panels], Danny Sullivan, Google (2020).
* [https://www.aboutamazon.com/news/innovation-at-amazon/making-search-easier 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.)''
* [https://www.amazon.science/blog/building-product-graphs-automatically Building product graphs automatically], Xin Luna Dong, Amazon (2020).
* [https://json-ld.org/ JSON for Linking Data]
* [[:File:S07-EnterpriseKGs.pdf | Slides from the lecture]]


=Lectures=
Supplementary readings:
Below are the mandatory and suggested readings for each lecture. All the text-book chapters are mandatory.
* Parts 2 and 4 in Blumauer & Nagy's text book (''strongly suggested - this is where Blumauer & Nagy's book is good!'')
* [[:File:Bosch-LIS.pdf | 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.
* [[:File:2006.13473.pdf | 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 1: Introduction==
==Lecture 8: Rules (SHACL and RDFS)==
* Chapters 1-2 in Allemang & Hendler. ''In text book.''
* Tim Berners-Lee about the semantic web. <http://www.youtube.com/watch?v=HeUrEh-nqtU> (mandatory)
* [http://jena.apache.org/about_jena/ Welcome to Apache Jena] (useful starting page)
* [http://jena.apache.org/index.html Apache Jena] main page (useful starting page)
* [http://jena.apache.org/documentation/rdf/index.html The core RDF API] (mandatory)
* [http://jena.apache.org/tutorials/ Jena tutorials] (useful starting page)
* [http://jena.apache.org/tutorials/rdf_api.html An introduction to RDF and the Jena RDF API] (mandatory)
* [https://jena.apache.org/documentation/javadoc/jena/ Package org.apache.jena.rdf.model] (supplementary, but necessary for the labs and project - lab 1 and the lecture notes lists the classes and methods you should look at)


==Lecture 2: RDF (tentative)==
Themes:
* Chapters 3 in Allemang & Hendler. ''In text book.''
* SHACL and RDFS
* RDF 1.1 Primer
* Axioms, rules and entailment
* RDF 1.1 Semantics
* Programming SHACL and RDFS in Python
* RDF 1.1 Concepts and Abstract Syntax


==Lecture 3: RDFS (tentative)==
Mandatory readings:
* Chapters 6-7 in Allemang & Hendler. ''In text book.''
* Chapters 7-8 in Allemang, Hendler & Gandon (3rd edition)
* RDF Schema 1.1
* [https://book.validatingrdf.com/bookHtml011.html Chapter 5 ''SHACL''] in [https://book.validatingrdf.com/index.html Validating RDF] (available online)
* Reasoners and rules engines: Jena inference support
** Sections 5.1, 5.3-5.5, and 5.6,1-5.6.3
* [http://www.w3.org/TR/rdf-schema/ W3C's RDF Schema 1.1], focus on sections 1-3 and 6
* [[:File:S07-SHACL-RDFS.pdf | Slides from the lecture]]


==Lecture 4: Architecture (tentative)==
Useful materials:
* Chapter 4 in Allemang & Hendler. ''In text book.''
* Interactive, online [https://shacl.org/playground/ SHACL Playground]
* [https://docs.google.com/presentation/d/1weO9SzssxgYp3g_44X1LZsVtL0i6FurQ3KbIKZ8iriQ/ Lab presentation containing a short overview of SHACL and pySHACL]
* [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)]
* [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 review them in the lecture'')
* [https://github.com/blazegraph/database/wiki/InferenceAndTruthMaintenance Inference and Thruth Maintenance in Blazegraph]
* [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 - check the [[Python Examples]] first
* Pages 101-106 in Blumauer & Nagy (suggested)


==Lecture 5: Services (tentative)==
==Lecture 9: Ontologies (OWL)==
* JSON Syntax
* Section 2 in JSON-LD 1.0 Processing Algorithms and API
* JSON-LD: Introductory Material (videos)


==Lecture 6: SPARQL (tentative)==
Themes:
* Chapter 5 in Allemang & Hendler. ''In text book.''
* Basic OWL concepts
* SPARQL 1.1 Query Language
* Axioms, rules and entailments
* SPARQL 1.1 Update Language
* Programming basic OWL in Python
* SPARQL 1.1 Overview


==Lecture 7: Visualisation (tentative)==
Mandatory readings:
* Shneiderman, Ben (1996): The eyes have it: A task by data type taxonomy for information visualizations. ''Paper.'' [http://drum.lib.umd.edu/bitstream/handle/1903/466/CS-TR-3665.pdf?sequence=2]
* Chapter 9-10, 12-13 in Allemang, Hendler & Gandon (3rd edition)
* Heer & Shneiderman (2012): Interactive Dynamics for Visual Analysis - A taxonomy of tools that support the fluent and flexible use of visualizations. ''Paper.'' [http://dl.acm.org/ft_gateway.cfm?id=2146416&ftid=1146182&dwn=1]
* [http://www.w3.org/TR/owl-primer OWL2 Primer], sections 2-6 and 9-10
* Skjæveland 2012: Sgvizler. ''Paper.'' [http://www.eswc2012.org/sites/default/files/eswc2012_submission_303.pdf]
* [http://vowl.visualdataweb.org/ VOWL: Visual Notation for OWL Ontologies]
* Sgvizler 0.6
* [https://protegeproject.github.io/protege/getting-started/ Protégé-OWL Getting Started]
* [http://vowl.visualdataweb.org/v2/ VOWL: Visual Notation for OWL Ontologies]
* [[:File:S09-OWL.pdf | Slides from the lecture]]
 
Useful materials (cursory):
* [http://www.w3.org/TR/owl-overview OWL 2 Document Overview]
* [https://www.w3.org/TR/owl2-quick-reference/ OWL 2 Quick Reference Guide]
* [https://www.w3.org/TR/owl2-rdf-based-semantics/ OWL2 RDF-Based Semantics]
* The OWL-RL materials (from Lecture 5)
* [http://vowl.visualdataweb.org/v2 VOWL: Visual Notation for OWL Ontologies]
* [http://vowl.visualdataweb.org/webvowl/index.html#sioc WebVOWL]
* [http://vowl.visualdataweb.org/webvowl/index.html#sioc WebVOWL]
* [[:File:LohmannEtAl2016-VisualizingOntologiesWithVOWL.pdf | 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)
* [http://lov.okfn.org/dataset/lov/ Linked Open Vocabularies (LOV)]
* Important vocabularies / ontologies:
** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)
** [http://motools.sourceforge.net/event/event.html Event Ontology (event)]
** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]
** [https://www.w3.org/2003/01/geo/ geo: World Geodetic Standard (WGS) 84]
** [http://dublincore.org/ Dublin Core (DC)]
** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]
** [http://rdfs.org/sioc/spec/ Semantic Interlinked Online Communities (SIOC)]
** [http://schema.org/docs/full.html schema.org - Full Hierarchy]
** [http://wikidata.dbpedia.org/services-resources/ontology DBpedia Ontology]
** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]
** [http://creativecommons.org/ns 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.
* [[:File:S10-Vocabularies.pdf | Slides from the lecture]]
==Lecture 11: KG embeddings==
Themes:
* KG embeddings
* Link prediction
* TorchKGE
Mandatory readings:
* [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:S11-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 12: KGs and Large Language Models==


==Lecture 8: RDFS Plus (tentative)==
Themes:
* Chapter 8 in Allemang & Hendler. ''In text book.''


==Lecture 9: Vocabularies (tentative)==
* What are Large Language Models (LLMs)
* Chapters 9-10 and 13 in Allemang & Hendler. ''In text book.''
* Combining KGs and Large Language Models (LLMs)
* Linked Open Vocabularies (LOV)
** retrieval augmented knowledge fusion
** end-to-end KG construction
** LLM-augmented KG to text generation


==Lecture 10: Linked Open Data (LOD) (tentative)==
Mandatory readings:
* LODstats
* The Linking Open Data cloud diagram
* State of the LOD Cloud 2014


==Lecture 11: Resources (tentative)==
* [[:file:S12-KGsAndLLMs.pdf | Slides from the lecture]]
* Dbpedia
* No mandatory readings beyond the slides
* Wikidata
* GeoNames
* WordNet - A lexical database for English


==Lecture 12: OWL (tentative)==
Supplementary readings:
* Chapters 11-12 in Allemang & Hendler. ''In text book.''
* OWL2 Overview
* OWL2 Primer
* OWL2 Web Ontology Language Manchester Syntax
* Jena Ontology API


==Lecture 13: OWL DL (tentative)==
* Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024). [[:file:PanEtAl2023-LLMs_KGs_Opportunities_Challenges.pdf | ''Unifying large language models and knowledge graphs: A roadmap.'']] IEEE Transactions on Knowledge and Data Engineering.
* Nardi & Brachman: Introduction to Description Logics. Chapter 1 in Description Logic Handbook. ''Paper.'' [https://www.google.no/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiO66mdqeLQAhXBoCwKHXnND3QQFggfMAA&url=https%3A%2F%2Fwww.inf.unibz.it%2F~franconi%2Fdl%2Fcourse%2Fdlhb%2Fdlhb-01.pdf&usg=AFQjCNESKoc0OVEyV54mB2gZekZcOdZDsg&sig2=sZPu2Nk5I06zl7IJcII3ZA]
* Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). [[:file:NIPS-2017-attention-is-all-you-need-Paper.pdf | ''Attention is all you need.'']] Advances in neural information processing systems, 30.<br />
* Baader & Nutt: Basic Description Logics. Chapter 2 in Description Logic Handbook. ''Paper.'' [https://www.google.no/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjo0NWoqeLQAhXChSwKHWasBuoQFggiMAE&url=https%3A%2F%2Fwww.inf.unibz.it%2F~franconi%2Fdl%2Fcourse%2Fdlhb%2Fdlhb-02.pdf&usg=AFQjCNH46M6Vh01GE6Z6zhajePPVd99cdg&sig2=3R0-B-uNmwLnDwijEHGILQ&bvm=bv.140496471,d.bGg]
* Complexity of Reasoning in Description Logics [http://www.cs.man.ac.uk/~ezolin/dl/]


==Lecture 14: Ontology development (tentative)==
&nbsp;
* Chapters 14-16 in Allemang & Hendler. ''In text book.''
<div class="credits" style="text-align: right; direction: ltr; margin-left: 1em;">''INFO216, UiB, 2017-2024, Andreas L. Opdahl (c)''</div>
* Noy & McGuinness (2001): Ontology Development 101: A Guide to Creating Your First Ontology. ''Paper.'' [https://www.google.no/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiCpeSDq-LQAhWICywKHX92CRQQFgghMAA&url=http%3A%2F%2Fprotege.stanford.edu%2Fpublications%2Fontology_development%2Fontology101.pdf&usg=AFQjCNGe3FD2J8jtwome1E6cqfsBVT6TcQ&sig2=5SMb-L2ZKY_J86NU5YG6dQ]
* Sicilia et al. (2012): Empirical findings on ontology metrics. ''Paper.'' [http://www.sciencedirect.com/science/article/pii/S095741741101640X]

Latest revision as of 14:23, 14 May 2024

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:

Useful materials:

  • Important knowledge graphs (which we will look more at later):
  • 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:

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: Linked Open Data (LOD)

Themes:

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

Mandatory readings (both lecture 4 and 5):

Useful materials

Lecture 5: Open Knowledge Graphs I

Themes:

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

Mandatory readings:

Lecture 6: Open Knowledge Graphs II

Themes:

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

Mandatory readings:

Useful materials

Lecture 7: Enterprise Knowledge Graphs

Themes:

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

Mandatory readings:

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:

Useful materials:

Lecture 9: Ontologies (OWL)

Themes:

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

Mandatory readings:

Useful materials (cursory):

Lecture 10: Vocabularies

Themes:

  • LOD vocabularies and ontologies

Mandatory readings:


Lecture 11: KG embeddings

Themes:

  • KG embeddings
  • Link prediction
  • TorchKGE

Mandatory readings:

Supplementary readings:

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:

Supplementary readings:

 

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