Curriculum

This page introduces EUCLID's training curriculum. The goal of the here presented training program is to offer learning support to data practitioners, covering different levels of knowledge and experience. In particular, the individual modules are structured in such a way that they can be used as building blocks for acquiring skills and knowledge but also be viewed individually, depending on the area of interest.

We welcome comments and feedback on the presented curriculum!

Since all the produced materials are freely available and we aim to achieve a high level of distribution and reuse, we would be glad to consider recommendations, suggestions and topics, which the community and targeted audience, see as relevant.

  • We welcome comments on the individual chapters, as well as on the curriculum and the covered topics as a whole.
  • We would be glad to link to further available reading or presentation materials.
  1. 1 Introduction and Application Scenarios
    1. 1.1 Introduction
    2. 1.2 Motivation of the Course
    3. 1.3 Background Technologies
      1. Internet
      2. Hypertext
      3. WWW
      4. Web 1.0 (static)
      5. Web 2.0 (dynamic)
      6. Social Web
      7. Web 3.0 (semantic)
      8. Ontologies
    4. 1.4 Background Standards
      1. HTTP
      2. URI
      3. XML
      4. RDF
      5. RDFS
      6. OWL (OWL 2 Full, OWL 2 DL, OWL 2 EL, OWL 2 QL, OWL 2 RL)
      7. SPARQL
    5. 1.5 Linked Data
      1. Linked Data Principles
      2. Rating Published Datasets
      3. Growth of Linked Data on the Web
    6. 1.6 Case Scenario: a Music Portal
    7. 1.7 Examples
      1. Marbles
      2. Sigma
      3. DBpedia Mobile
  2. 2 Querying Linked Data
    1. 2.1 Introduction and Motivation Scenario
    2. 2.2 SPARQL Terminology
    3. 2.3 Querying and Updating Linked Data with SPARQL
      1. Introduction to SPARQL
      2. Querying Linked Data with SPARQL
        1. Query forms: ASK, SELECT, DESCRIBE, CONSTRUCT
        2. Query patterns: BGP, UNION, OPTIONAL, FILTER
        3. Sequence modifiers: DISTINCT, REDUCED, ORDER BY, LIMIT, OFFSET
      3. Updating Linked Data with SPARQL 1.1
        1. Data management: INSERT, DELETE; DELETE/INSERT
        2. Graph management: LOAD, CLEAR, CREATE, DROP, COPY/MOVE/ADD
      4. SPARQL Protocol: query operation, update operation
    4. 2.4 Reasoning over Linked Data
      1. SPARQL 1.1 entailment regimes
      2. RDFS entailment regimes, lacks of consistency check, inference limitations
      3. OWL properties, property axioms, axioms, class constructions
  3. 3 Providing Linked Data
    1. 3.1 Introduction and Motivation
    2. 3.2 Linked Data Lifecycle
      1. Linked Data Principles
      2. Tasks fro Providing Linked Data
    3. 3.3 Creating Linked Data
      1. Data extraction, giving names (URIs), selecting vocabularies
    4. 3.4 Interlinking Linked Data
      1. Link discovery
      2. Manual interlinking, automatic interlinking
      3. Interlinking with SKOS
    5. 3.5 Publishing Linked Data
      1. Describing dataset with metadata (VoID)
      2. Making the dataset accessible (dereferencing HTTP URIs, RDF dump, SPARQL endpoint, RDFa)
      3. Making the dataset searchable (search engine support)
      4. Exposing the dataset in repositories (creating new ones - CKAN, using the Data Hub, the Linking Open Data Cloud)
    6. 3.6 Linked Data Publishing Checklist
    7. 3.7 Tools for Providing Linked Data
      1. OpenRefine: Extracting data from spreadsheets
      2. R2RML: Extracting data from RDBMS
      3. GATECLOUD: Extracting data from text
      4. CALAIS: Extracting data from text
      5. Silk: Interlinking data sets
  4. 4 Interaction with Linked Data
    1. 4.1 Introduction and Motivations
    2. 4.2 Linked Data Visualisation
      1. Visualisation Techniques
        1. Challenges for Linked Data Visualization
        2. Classification of Visualization Techniques
        3. Applications of Linked Data Visualization Techniques
      2. Linked Data Visualization Tools
        1. Linked Data Visualization Tool Requirements
        2. Linked Data Visualization Tool Types
        3. Linked Data Visualization Examples
          1. Sig.ma
          2. Sindice
          3. Information Workbench
          4. LOD live
          5. LOD Visulalization
      3. Linking Open Data Cloud Visualization
        1. "The Linking Open Data cloud diagram" by Richard Cyganiak and Anja Jentzsch
        2. "Linked Open Data Cloud" generated by Gephis
        3. "Linked Open Data Graph" by Protovis
      4. LD Reporting
        1. Google Webmaster Tool
    3. 4.3 Linked Data Search
      1. Semantic Search Process
      2. Semantic Search and Linked Data
        1. Semantic Search vs. SPARQL query
        2. Semantic Search with Google
        3. Semantic Search with DuckDuckGo
      3. Faceted Search
        1. Information Workbench
        2. FacetedDBLP
      4. Classification of Search Engines
        1. Semantic Data Search Engines
          1. Swoogle
          2. Watson
        2. Searching for Vocabularies
          1. LOV Portal
        3. Searching for Documents
          1. Semantic Wen Search Engine (SWSE)
          2. Sindice
    4. 4.4 Methods for Linked Data Analysis
      1. Features of Linked Data analysis
        1. Data Aggregation and Filtering
        2. Statistical analysis
          1. R for SPARQL
        3. Machine learning
  5. 5 Creating Linked Data Applications
    1. 5.1 Introduction and Motivations
    2. 5.2 Linked Data Applications
      1. Characterization of Linked Data Applications
      2. Categories of Linked Data Applications
      3. Examples
        1. Data.gov.uk
        2. Data.gov
        3. BBC – Dynamic Semantic Publishing
        4. ResearchSpace
        5. Open Pharmacology Space
        6. Information Workbench
        7. eCloudManager – Integrated View on the Data Center
    3. 5.3 Using Web APIs
      1. Underlying Technology Basics
      2. Web APIs - Motivation
      3. Richardson Maturity Model for REST Services
      4. Well-Known Web APIs
        1. Freebase API
        2. Twitter API
        3. Last.fm API
        4. Foursquare API
        5. Amazon S3
    4. 5.4 Linked Data application architecture
      1. Software Architecture
        1. Client-Server Model
        2. Multitier Architecture
      2. Architecture of Linked Data Applications
        1. Linked Data Architectural Patterns
        2. General Architecture of Linked Data Applications
          1. Publication Layer
          2. Data Access Component
          3. Data Integration Component
          4. Data Layer
          5. Application and Presentation Layers
      3. Challenges for Developing Linked Data Applications
    5. 5.5 Linked Data application development frameworks
      1. Information Workbench Architecture
        1. Ontology as a "structural backbone" of the application
        2. Managing data
          1. Connecting to the data repository
          2. Integrating external data using data providers
          3. Federated data access
        3. Creating the user interface
          1. Data-driven UI: Providing views over data resources
          2. Ontology-driven UI structure:
            1. Class views: providing overview over the dataset
            2. Wiki templates: common UI structure for data instances
        4. Constructing a mashup
        5. Data authoring
  6. 6 Scaling‐up Linked Data
    1. 6.1 Introduction to Big Linked Data
      1. Characterization of Big Data - the 3 Vs
      2. Types of Big Data
      3. Operators and Tools for Big Data Applications
      4. Towards Big Linked Data
    2. 6.2 Scaling Storage for Linked Data
      1. NoSQL databases for RDF management
        1. Advantages of Using NoSQL Systems
        2. Key/Value Stores
        3. Wide-Column Stores
          1. HBase
        4. Document Databases
          1. Couchbase
        5. Graph Databases
          1. Neo4j
        6. RDF Native Stores
          1. 4store
          2. CumulusRDF
    3. 6.3 Scaling Reasoning for Linked Data
      1. Challenges of Reasoning over Big Linked Data
      2. QueryPIE - Hybrid Reasoning
      3. Working with Distributed Data
        1. Hadoop and MapReduce
      4. RDFS Reasoning with Hadoop
      5. Massively Parallel Processing
      6. RDFS Reasoning with Graphics Processing Units (GPUs)
      7. Stream Processing
    4. 6.4 Generating and accessing high-velocity Semantic Data
      1. Linked Stream/Sensor Data
      2. Semantic Sensors Data
      3. Querying Streams with SPARQL
        1. C-SPARQL and SPARQLStream

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