MediaWiki API result

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            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/postorius/lists/mediawiki-api-announce.lists.wikimedia.org/> for notice of API deprecations and breaking changes."
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                        "*": "\n=Textbooks=\n\nMain course book (''the whole book is mandatory reading''):\n* 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. \n\nSupplementary reading book (''not'' mandatory):\n* Andreas Blumauer and Helmut Nagy (2020). '''The Knowledge Graph Cookbook - Recipes that Work.''' mono/monochrom. ISBN-10: \u200e3902796707, ISBN-13: 978-3902796707.\n\n=Other materials=\n\nIn 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.\n\n'''The lectures and lectures notes are also part of the curriculum.'''\n\nMake 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.\n\n''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.\n\n=Lectures (in progress)=\n\nBelow 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.\n\n==Lecture 1: Introduction to KGs==\n\nThemes:\n* Introduction to Knowledge Graphs\n* Organisation of the course\n\nMandatory readings:\n* Chapters 1-2 in Allemang, Hendler & Gandon (3rd edition)\n* [http://www.youtube.com/watch?v=HeUrEh-nqtU Tim Berners-Lee talks about the semantic web]\n* [[:File:S01-KnowledgeGraphs.pdf | Slides from the lecture]]\n\nUseful materials:\n* Important knowledge graphs (''which we will look more at later''):\n** Wikidata (https://www.wikidata.org/)\n<!-- ** DBpedia (https://www.dbpedia.org, https://dbpedia.org/page/Bergen)\n** GeoNames (https://www.geonames.org/)\n** BabelNet (https://babelnet.org/)\n** Linking Open Data (LOD) (http://lod-cloud.net)\n** Linked Open Vocabularies (LOV, https://lov.linkeddata.es/dataset/lov/)\n-->\n* Pages 27-55 and 105-122 in Blumauer & Nagy (suggested)\n\n==Lecture 2: Representing KGs (RDF)==\n\nThemes: \n* Resource Description Framework (RDF)\n* Programming RDF in Python\n\nMandatory readings:\n* Chapter 3 in Allemang, Hendler & Gandon (3rd edition)\n* [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)\n* [http://rdflib.readthedocs.io/ RDFlib 7.0.0 documentation], the following pages:\n** The main page\n** Getting started with RDFLib\n** Loading and saving RDF\n** Creating RDF triples\n** Navigating Graphs\n** Utilities and convenience functions\n** RDF terms in rdflib\n** Namespaces and Bindings\n* [[:File:S02-RDF.pdf | Slides from the lecture]]\n\nUseful materials:\n* [https://rdflib.readthedocs.io/en/stable/apidocs/modules.html RDFLib 7.0.0 packages] (reference for the labs)\n* [https://www.ldf.fi/service/rdf-grapher RDF Grapher] for drawing RDF graphs\n* [https://issemantic.net/rdf-visualizer RDF Visualizer] for drawing RDF graphs\n* [https://www.w3.org/TR/rdf11-concepts/ W3C's RDF 1.1 Concepts and Abstract Syntax]\n<!-- * An overview page of some other [https://www.w3.org/2018/09/rdf-data-viz/ RDF Data Visualization tools] -->\n* Pages 25-28, 92-100, 125-128, and 164-167 in Blumauer & Nagy (suggested)\n\n==Lecture 3: Querying and updating KGs (SPARQL)==\n\nThemes:\n* SPARQL queries\n* SPARQL Update\n* Programming SPARQL and SPARQL Update in Python\n\nMandatory readings (tentative):\n* Chapter 6 in Allemang, Hendler & Gandon (3rd edition)\n* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language] (Sections 1-3)\n* [https://rdflib.readthedocs.io/ rdflib 7.0.0] materials:\n** [https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html Querying with SPARQL]\n* [[:File:S03-SPARQL.pdf | Slides from the lecture]]\n\nUseful materials:\n<!-- * [https://medium.com/wallscope/constructing-sparql-queries-ca63b8b9ac02 Constructing SPARQL Queries] -->\n* [http://www.w3.org/TR/sparql11-query/ SPARQL 1.1 Query Language]\n* [http://www.w3.org/TR/sparql11-update/ SPARQL 1.1 Update Language] (the rest of it)\n* [[:File:sparql-1_1-cheat-sheet.pdf | SPARQL 1.1 Cheat Sheet]]\n* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]\n* For example pages 54-55, 133 in Blumauer & Nagy (suggested)\n* The [[:File:kg4news-dump-20230130.txt | Knowledge Graphs for the News]] example used in the lecture. (Remember to save with the correct ''.ttl'' extension.)\n\n==Lecture 4: Linked Open Data (LOD)==\n\nThemes:\n* Linked Open Data(LOD)\n* The LOD cloud\n* Data provisioning\n\nMandatory readings ''(both lecture 4 and 5)'':\n* Chapter 5 in Allemang, Hendler & Gandon (3rd edition)\n* [https://www.w3.org/DesignIssues/LinkedData.html Linked Data], Tim Berners-Lee, 2006-07-27.\n* [[:File:S04-LOD.pdf | Slides from the lecture]]\n\nUseful materials\n* [https://www.ontotext.com/knowledgehub/fundamentals/linked-data-linked-open-data/ What Are Linked Data and Linked Open Data?]\n* [[: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.]]\n\n==Lecture 5: Open Knowledge Graphs I==\n\nThemes:\n* Important open KGs (LOD datasets)\n** Wikidata\n** DBpedia\n\nMandatory readings:\n* Chapter 5 in Allemang, Hendler & Gandon (3rd edition)\n* Important knowledge graphs - and what to read:\n** Wikidata (https://www.wikidata.org/):\n*** [https://www.wikidata.org/wiki/Wikidata:Introduction Introduction to Wikidata]\n*** [https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/Wikidata_Query_Help SPARQL query service/A gentle introduction to the Wikidata Query Service]\n*** example: [https://www.wikidata.org/wiki/Q26793]\n** DBpedia (https://www.dbpedia.org):\n*** [http://wiki.dbpedia.org/about About Dbpedia]\n*** example: [https://dbpedia.org/resource/Bergen]\n*  [[:File:S05-S06-OpenKGs.pdf | Slides from the lecture]]\n\n==Lecture 6: Open Knowledge Graphs II==\n\nThemes:\n* Important open KGs (LOD datasets)\n** DBpedia ''(continued)''\n** GeoNames\n** the GDELT project\n** WordNet\n** BabelNet\n** ConceptNet\n\nMandatory readings:\n* Chapter 5 in Allemang, Hendler & Gandon (3rd edition)\n* Important knowledge graphs - and what to read:\n** GeoNames (https://www.geonames.org/):\n*** [http://www.geonames.org/about.html About GeoNames]\n*** example: [https://www.geonames.org/3161732/bergen.html]\n** GDELT (https://www.gdeltproject.org/)\n*** [https://www.gdeltproject.org/ The GDELT Project] - see also the About and Data pages\n** WordNet (https://wordnet.princeton.edu/)\n*** [https://wordnet.princeton.edu/ WordNet - A lexical database for English]\n** BabelNet (https://babelnet.org/):\n*** [http://live.babelnet.org/about About BabelNet]\n*** [https://babelnet.org/how-to-use How to use]\n*** example: [https://babelnet.org/synset?id=bn%3A00010008n&orig=Bergen&lang=EN]\n** ConceptNet (http://conceptnet.io)\n*** [http://conceptnet.io ConceptNet - An open, multilingual knowledge graph]\n*  [[:File:S05-S06-OpenKGs.pdf | Slides from the lecture]]\n\nUseful materials\n* Wikidata statistics\n** [https://grafana.wikimedia.org/d/000000167/wikidata-datamodel?orgId=1&refresh=30m Entity statistics]\n** [https://grafana.wikimedia.org/d/000000175/wikidata-datamodel-statements?orgId=1&refresh=30m Statement statistics]\n* [https://www.dbpedia-spotlight.org/ DBpedia Spotlight]\n* GDELT documentation\n** [http://data.gdeltproject.org/documentation/GDELT-Event_Codebook-V2.0.pdf Event Codebook (and covers mentions)]\n** [http://data.gdeltproject.org/documentation/CAMEO.Manual.1.1b3.pdf CAMEO event codes and other codes]\n** [http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook-V2.1.pdf Global Knowledge Graph Codebook]\n* Parts 1 and 3 in Blumauer & Nagy's text book (not tightly related to the lecture, but time to finish them by now :-))\n\n==Lecture 7: Enterprise Knowledge Graphs==\n\nThemes: \n* Enterprise Knowledge Graphs (EKGs)\n* Google\u2019s Knowledge Graph\n* Amazon\u2019s Product Graph\n* JSON-LD (video presentation)\n\nMandatory readings:\n* [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.)''\n* [https://blog.google/products/search/about-knowledge-graph-and-knowledge-panels/ A reintroduction to our Knowledge Graph and knowledge panels], Danny Sullivan, Google (2020).\n* [https://www.aboutamazon.com/news/innovation-at-amazon/making-search-easier How Amazon\u2019s 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.)''\n* [https://www.amazon.science/blog/building-product-graphs-automatically Building product graphs automatically], Xin Luna Dong, Amazon (2020).\n* [https://json-ld.org/ JSON for Linking Data]\n* [[:File:S07-EnterpriseKGs.pdf | Slides from the lecture]]\n\nSupplementary readings:\n* Parts 2 and 4 in Blumauer & Nagy's text book (''strongly suggested - this is where Blumauer & Nagy's book is good!'')\n* [[:File:Bosch-LIS.pdf | LIS: A knowledge graph-based line information system]] by Grangel-Gonz\u00e1lez, I., Rickart, M., Rudolph, O., & Shah, F. (2023, May). In Proceedings of the European Semantic Web Conference (pp. 591-608). Cham: Springer Nature Switzerland.\n* [[: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 :-)''\n\n==Lecture 8: Rules (SHACL and RDFS)==\n\nThemes:\n* SHACL and RDFS\n* Axioms, rules and entailment\n* Programming SHACL and RDFS in Python\n\nMandatory readings:\n* Chapters 7-8 in Allemang, Hendler & Gandon (3rd edition)\n* [https://book.validatingrdf.com/bookHtml011.html Chapter 5 ''SHACL''] in [https://book.validatingrdf.com/index.html Validating RDF] (available online)\n** Sections 5.1, 5.3-5.5, and 5.6,1-5.6.3\n* [http://www.w3.org/TR/rdf-schema/ W3C's RDF Schema 1.1], focus on sections 1-3 and 6\n* [[:File:S07-SHACL-RDFS.pdf | Slides from the lecture]] \n\nUseful materials:\n* Interactive, online [https://shacl.org/playground/ SHACL Playground]\n* [https://docs.google.com/presentation/d/1weO9SzssxgYp3g_44X1LZsVtL0i6FurQ3KbIKZ8iriQ/ Lab presentation containing a short overview of SHACL and pySHACL]\n* [https://pypi.org/project/pyshacl/ pySHACL - A Python validator for SHACL at PyPi.org] ''(after installation, go straight to \"Python Module Use\".)''\n* [https://w3c.github.io/data-shapes/shacl/ Shapes Constraint Language (SHACL) (Editor's Draft)]\n* [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'')\n* [https://github.com/blazegraph/database/wiki/InferenceAndTruthMaintenance Inference and Thruth Maintenance in Blazegraph]\n* [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''.\n* [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\n* Pages 101-106 in Blumauer & Nagy (suggested)\n\n==Lecture 9: Ontologies (OWL)==\n\nThemes:\n* Basic OWL concepts\n* Axioms, rules and entailments\n* Programming basic OWL in Python\n\nMandatory readings:\n* Chapter 9-10, 12-13 in Allemang, Hendler & Gandon (3rd edition)\n* [http://www.w3.org/TR/owl-primer OWL2 Primer], sections 2-6 and 9-10\n* [http://vowl.visualdataweb.org/ VOWL: Visual Notation for OWL Ontologies]\n* [https://protegeproject.github.io/protege/getting-started/ Prot\u00e9g\u00e9-OWL Getting Started]\n* [[:File:S09-OWL.pdf | Slides from the lecture]]\n\nUseful materials (cursory):\n* [http://www.w3.org/TR/owl-overview OWL 2 Document Overview]\n* [https://www.w3.org/TR/owl2-quick-reference/ OWL 2 Quick Reference Guide]\n* [https://www.w3.org/TR/owl2-rdf-based-semantics/ OWL2 RDF-Based Semantics]\n* The OWL-RL materials (from Lecture 5)\n* [http://vowl.visualdataweb.org/v2 VOWL: Visual Notation for OWL Ontologies]\n* [http://vowl.visualdataweb.org/webvowl/index.html#sioc WebVOWL]\n* [[:File:LohmannEtAl2016-VisualizingOntologiesWithVOWL.pdf | Lohmann et al. (2019): Visualizing Ontologies with VOWL. ''Semantic Web Journal.'']]\n* Pages 106-109 in Blumauer & Nagy (suggested)\n\n==Lecture 10: Vocabularies==\n\nThemes:\n* LOD vocabularies and ontologies\n\nMandatory readings:\n* Chapters 10-11 in Allemang, Hendler & Gandon (3rd edition)\n* [http://lov.okfn.org/dataset/lov/ Linked Open Vocabularies (LOV)]\n* Important vocabularies / ontologies:\n** [http://xmlns.com/foaf/spec/ Friend of a Friend (FOAF)] (if necessary follow the link to the 2004 version)\n** [http://motools.sourceforge.net/event/event.html Event Ontology (event)]\n** [http://www.w3.org/TR/owl-time/ Time ontology in OWL (time, OWL-time)]\n** [https://www.w3.org/2003/01/geo/ geo: World Geodetic Standard (WGS) 84]\n** [http://dublincore.org/ Dublin Core (DC)]\n** [http://www.w3.org/2004/02/skos/ SKOS - Simple Knowledge Organization System Home Page]\n** [http://rdfs.org/sioc/spec/ Semantic Interlinked Online Communities (SIOC)]\n** [http://schema.org/docs/full.html schema.org - Full Hierarchy]\n** [http://wikidata.dbpedia.org/services-resources/ontology DBpedia Ontology]\n** [http://www.w3.org/ns/prov# Provenance Interchange (PROV)]\n** [http://creativecommons.org/ns Creative Commons (CC) Vocabulary]\n** ''What we expect you to know about each vocabulary is this:'' \n*** Its purpose and where and how it can be used.\n*** Its most central 3-6 classes and properties be able to explain its basic structure. \n*** 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. \n* [[:File:S10-Vocabularies.pdf | Slides from the lecture]]\n\n\n==Lecture 11: KG embeddings==\n\nThemes:\n* KG embeddings\n* Link prediction\n* TorchKGE\n\nMandatory readings:\n* [https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08 Introduction to Machine Learning for Beginners] ([[:file:IntroToMachineLearning.pdf | PDF]])\n* [https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa Introduction to Word Embeddings and word2vec] ([[:file:IntroToWordEmbeddings.pdf | PDF]])\n* [https://towardsdatascience.com/introduction-to-knowledge-graph-embedding-with-dgl-ke-77ace6fb60ef Introduction to Knowledge Graph Embeddings] ([[:file:IntroToKGEmbeddings.pdf | PDF]])\n* [[:file:S11-GraphEmbeddings.pdf | Slides from the lecture]]\n\nSupplementary readings:\n* [[:file:Mikolov_et_al._-_2013_-_Efficient_Estimation_of_Word_Representations_in_Ve.pdf | Mikolov et al\u2019s original word2vec paper]]\n* [[:file:Bordes_et_al._-_Translating_Embeddings_for_Modeling_Multi-relation.pdf | Bordes et al\u2019s original TransE paper]]\n* [https://torchkge.readthedocs.io/en/latest/ Welcome to TorchKGE\u2019 s documentation!] (for the labs)\n\n==Lecture 12: KGs and Large Language Models==\n\nThemes:\n\n* What are Large Language Models (LLMs)\n* Combining KGs and Large Language Models (LLMs)\n** retrieval augmented knowledge fusion\n** end-to-end KG construction\n** LLM-augmented KG to text generation\n\nMandatory readings:\n\n* [[:file:S12-KGsAndLLMs.pdf | Slides from the lecture]]\n* No mandatory readings beyond the slides\n\nSupplementary readings:\n\n* 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.\n* 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 />\n\n&nbsp;\n<div class=\"credits\" style=\"text-align: right; direction: ltr; margin-left: 1em;\">''INFO216, UiB, 2017-2024, Andreas L. Opdahl (c)''</div>"
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                "title": "Russian investigation KG",
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                        "*": "The file ''russia-investigation_kg.ttl'' contains an RDF graph extracted from the data behind the FiveThirtyEight story [https://projects.fivethirtyeight.com/russia-investigation/ Is The Russia Investigation Really Another Watergate?].\n\nThe knowledge graph contains kay data about every special investigation since the Watergate probe began in 1973 and who was charged in them.\n\nHere are explanations of the properties used in the graph:\n* ''investigation:'' Unique id for each investigation.\n* ''investigation-start:'' Start date of the investigation.\n* ''investigation-end:'' End date of the investigation.\n* ''investigation-days:'' Length, in days, of the investigation. Days will be negative if the charge occured before the investigation began.\n* ''name:'' Name of the person charged (if applicable). Will be blank if there were no charges.\n* ''indictment-days :'' Length, in days, from the start of the investigation to the date the person was charged (if applicable). Days will be negative if the charge occured before the investigation began.\n* ''type:'' Result of charge (if applicable).\n* ''cp-date:'' Date the person pled guilty or was convicted (if applicable).\n* ''cp-days:'' Length, in days, from the start of the investigation to the date the person pled guilty or was convicted (if applicable).\n* ''overturned:'' Whether or not the relevant person's conviction was overturned.\n* ''pardoned:'' Whether or not the relevant person's charge was pardoned.\n* ''american:'' Whether or not the relevant person's charge was a U.S. resident.\n* ''president:'' President at the center of the investigation."
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