DKOU Summer School Resources

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Semantic web

Courses

1. Linked Data and the Semantic Web - Coursera course on the foundations of linked data and the Semantic Web.
2. Online lectures on Knowledge Graphs - Foundations and Applications
3. Knowledge Graphs and Retrieval-Augmented Generation (RAG) Short Course - A short course by DeepLearning.AI that explores the use of knowledge graphs and retrieval-augmented generation in AI applications.

Books (in English)

1. Semantic Web for the Working Ontologist - A guide to modeling semantic web data with RDF, RDFS, and OWL.
2. The Web of Data - An open access book by Aidan Hogan that provides an in-depth exploration of the concepts, technologies, and applications of the Semantic Web and Linked Data.
3. Linked Data - Evolving the Web into a Global Data Space - - a detailed technical introduction to Linked Data including the publication and consumption. type: a primer, authors: Tom Heath and Christian Bizer, publication year: 2011, ToC: Introduction / Principles of Linked Data / The Web of Data /Linked Data Design Considerations / Recipes for Publishing Linked Data / Consuming Linked Data / Summary and Outlook
4. Learning SPARQL: Querying and Updating with SPARQL 1.1 - This book teaches you how to use SPARQL 1.1 by starting you off with simple queries that demonstrate the language's query-by-example approach and then taking you through all the key features of the SPARQL 1.1 query and update languages.

• Books on Semantic Web
  • an information site on books (in English, French, Japanese, etc.) on the Semantic Web and Linked Data

Books (in Japanese)

• セマンティック・ウェブのためのRDF/OWL入門
  • type: a primer, authors: Masahide Kanzaki(神崎 正英), publication year: 2005, target audience: beginners, topics: Semantic Web, RDF, RDF schema, ontology, OWL, metadata, RDF search
• セマンティックWebとリンクトデータ
  • type: a primer, authors: Ken Kaneiwa(兼岩 憲), publication year**: 2017, target audience: beginners, topics: semantic web, RDF, 共通語彙, Linked Data, SPARQL
  • セマンティック・ウェブに関する主要トピック(RDF， 共通語彙，リンクトデータ，SPARQL等)の技術解説, toc: セマンティックWebとは/Webとデータ/Webデータ技術/セマンティックWeb技術とRDF/セマンティックWebの共通語彙/リンクトデータ/SPARQL
• Linked Data - Webをグローバルなデータ空間にする仕組み
  • type: a primer, authors: Tom Heath and Christian Bizer, translators: Hideaki Takeda(武田 英明), et al., published year: 2013, target audience: beginners
• オープンデータ時代の標準Web API　SPARQL
  • type: a primer, authors: Fumihiro Kato(加藤 文彦), et al., publication year: 2015, target audience: beginners
  • toc: なぜSPARQLが必要なのか？/SPARQLを支える技術/SPARQLの基本/SPARQLの言語仕様とクエリ/アプリケーション開発/クックブック

Semantic web (in general)

• An Introduction to the Semantic Web (6:29)
  • a short video used in a part of lessons called Introduction to the Semantic Web (Cambridge Semantics)
  • discusses the history of connecting information, from citations in documents to hyperlinks in the traditional World Wide Web to large-scale linking of data across databases, files and content within documents, and what is meant by Web 1.0, Web 2.0 and Web 3.0.

RDF

1. SPARQL Query Language for RDF - W3C Recommendation on the SPARQL query language.
2. An Introduction to RDF and the Jena RDF API
3. RDF 1.1 Primer - W3C Working Group Note, 24 June 2014. This primer is designed to provide the reader with the basic knowledge required to effectively use RDF. It introduces the basic concepts of RDF and shows concrete examples of the use of RDF. Secs. 3-5 can be used as a minimalist introduction into the key elements of RDF.

SPARQL

• SPARQL 1.1 Query Language - The official W3C Recommendation document for SPARQL 1.1.
• SPARQL Tutorial - A comprehensive tutorial on SPARQL from the W3C.
• Learning SPARQL - A comprehensive book by Bob DuCharme, covering everything from the basics to advanced SPARQL queries.
• SPARQL Tutorial by Apache Jena - A fast course in SPARQL.
• An Overview of SPARQL
  • type: a short video lecture (5:38), authors: ontotext, publication year: 2015
• Learning SPARQL: second edition sample data and queries - Sample data and queries from the book are available from the page.

Ontology

• Ontology Development 101: A Guide to Creating Your First Ontology
  • an ontology-development methodology for declarative frame-based systems including the steps in the ontology-development process and the complex issues of defining class hierarchies and properties of classes and instances.
  • type: a primer, topics: ontology, ontology development, authors: Natalya F. Noy and Deborah L. McGuinness

Linked Data

• What is Linked Data?
  • a short video for non-technical people

Experimental Data

• example data in physics
  • ESRF open data (870)
    • automatically collected metadata for the ESRF experiments (Only the proposers and experiment team can access these data during an embargo period of three years after the experiment, after which time the data will be automatically made public for other users to see and use. )

Metadata

• What is Metadata?
• Metadata for Data Management: A Tutorial
  • type: a tutorial site, authors; UNC, target audience: beginners

Metadata models

• examples
  • CSMD(Core Scientific Metadata Model)
    • a study-data oriented model which has been developed at STFC over many years. It captures high level information about scientific studies and the data that they produce.

Metadata schema

• examples
  • ICAT SCHEMA
    • a metadata schema based on CSMD. The ICAT metadata catalogue is a flexible solution for managing scientific metadata
  • OME

Data policy

• examples in physics
  • ESRF
    • ESRF Data Policy
      • ESRF Data Policy, Storage, and Services

Standards

• W3C standards and drafts
• Metadata Standards Catalog
  • The RDA Metadata Standards Catalog is a collaborative, open directory of metadata standards applicable to research data. It is offered to the international academic community to help address infrastructure challenges.

Aditional Resources :

1. RDF2Vec - A framework for creating RDF graph embeddings using neural language models.
2. Linked Open Data (LOD) Cloud - A visual representation and catalog of interlinked datasets published in the Linked Data format, showcasing the interconnections of various open data sources.

Databases:

1. [RIKEN metaDB] (http://metadbdev.riken.jp/)

Conferences:

• top conferences
  • ISWC2024
    • The 23rd International Semantic Web Conference(November 11 – 15, 2024)
  • ESWC2024
    • The Extended Semantic Web Conference (May 26 - 30, 2024)
• survey
  • ISWC/ESWC survey
    • survey of all ISWC/ESWC papers (in Japanese)
    • target: ISWC2020-2023, ESWC2022-2023

AI

Large Language Models (LLMs) Resources

1. Pretraining Large Language Models Short Course - A short course by DeepLearning.AI that covers the pretraining of large language models.
2. Deep Learning Specialization - Coursera specialization covering neural networks and deep learning, including LLMs.
3. Prompt Engineering with LLaMA 2 Short Course - A short course by DeepLearning.AI focused on prompt engineering techniques using the LLaMA 2 model.

Finetuning LLM

1. QLoRA: Efficient Finetuning of Quantized LLMs - A GitHub repo code for the efficient fine-tuning of quantized large language models (LLMs). Good if you don’t have access to big GPUs.
2. QLoRA: Training a Large Language Model on a 16GB GPU - An article on Towards AI detailing the process and techniques for training a large language model using QLoRA on a 16GB GPU.

NLP

1. Natural Language Processing with Deep Learning - Stanford University’s course on NLP with deep learning.
2. Dive into Deep Learning (D2L) - An interactive deep learning book with code, math, and discussions, covering a wide range of deep learning topics.
3. Efficient Fine-Tuning of LLMs: A Guide to LoRA - A Databricks blog post that provides a comprehensive guide on using LoRA (Low-Rank Adaptation) for the efficient fine-tuning of large language models.

Relevant Research Papers

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Tools & Frameworks

1. Hugging Face Transformers - A popular library for working with transformer models.
2. OpenAI - API for accessing OpenAI’s Playground.
3. spaCy - An industrial-strength NLP library with support for transformer models.

Communities & Forums

1. Hugging Face Forums - Community forums for discussing Hugging Face tools and models.
2. r/MachineLearning - A subreddit for machine learning discussions, including LLMs.
3. AI Alignment Forum - Discussions on AI alignment, safety, and ethics related to LLMs.

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