--Monday November 14th--
Meetup at Scaniarinken - Room: Powerplay

Getting to know semantic knowledge graphs
(120 min) Mandatory
* Introduction to Knowledge Graph and the Semantic Web Stack
* Resource Description Framework (RDF)
* RDF Schema (RDFS)
* Hands-on training on RDF/S: drawing and writing

-- Lunch break 60min --

Query language - SPARQLing data extraction
(90min)
Target audience: Developers
* Introduction to graph databases
* SPARQL query language
* Setting up Fuseki on your own laptop
* Uploading your RDF data
* Explore data with SPARQL queries

--Coffe break--

From spreadsheet to graph
(60min)
Target audience: Domain experts, developers
* Brief introduction to Reasonable Ontology Templates (OTTR)
* Sneak peak on Tuesday's agenda

Finish at 15:00

--Tuesday November 15th--
Meetup at Scaniarinken - Room: Powerplay

Wonders of ontologies
(120min) Target group: Domain experts, developers
* Mathematical foundations
* Web Ontology Language (OWL)
* Hands-on training on OWL: drawing and plotting in Protégé
* Visualization of ontology

--Lunch break--

Validating your data
(120min)
* Shapes Constraint Language (SHACL)
* Hands-on training on SHACL: SHACL-Playground

-- Coffe break --

Working with knowledge graphs in Java
(120 min)
Target: Developers
* Semantic Web for Java developers
* Handling RDF data models in RDF4J
* Tips and tricks on RDF serialization through Java

Finnish aprox. 16:30

**Knowledge graph** is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics underlying the used terminology

**Semantic Web** Semantic Web, sometimes known as Web 3.0 (not to be confused with Web3), is an extension of the World Wide Web through standards[1] set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used. These technologies are used to formally represent metadata. For example, ontology can describe concepts, relationships between entities, and categories of things. These embedded semantics offer significant advantages such as reasoning over data and operating with heterogeneous data sources

**Semantic Web Stack** The Semantic Web Stack is an illustration of the hierarchy of languages, where each layer exploits and uses capabilities of the layers below. It shows how technologies that are standardized for Semantic Web are organized to make the Semantic Web possible. It also shows how Semantic Web is an extension (not replacement) of classical hypertext web.

*Semantic Web Stack*

**The Resource Description Framework (RDF)** is a World Wide Web Consortium (W3C) standard originally designed as a data model for metadata. It has come to be used as a general method for description and exchange of graph data. RDF provides a variety of syntax notations and data serialization formats with Turtle (Terse RDF Triple Language) currently being the most widely used notation.

RDF is a directed graph composed of triple statements. An RDF graph statement is represented by: 1) a node for the subject, 2) an arc that goes from a subject to an object for the predicate, and 3) a node for the object. Each of the three parts of the statement can be identified by a URI. An object can also be a literal value. This simple, flexible data model has a lot of expressive power to represent complex situations, relationships, and other things of interest, while also being appropriately abstract.

**RDF Schema** (Resource Description Framework Schema, variously abbreviated as RDFS, RDF(S), RDF-S, or RDF/S) is a set of classes with certain properties using the RDF extensible knowledge representation data model, providing basic elements for the description of ontologies. It uses various forms of RDF vocabularies, intended to structure RDF resources. RDF and RDFS can be saved in a triplestore, then one can extract some knowledge from them using a query language, like SPARQL.

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**SPARQL** is an RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.

**A graph database** is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.[1] A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database.

**A Fuseki server** keeps detailed statistics for each dataset and each service of a dataset keeps counters as to the number of incoming requests, number of successful requests, number of bad requests (i.e client errors), and number of failing requests (i.e. server errors).

**Reasonable Ontology Templates (OTTR)** is a language with supporting tools for representing and instantiating RDF graph and OWL ontology modelling patterns. It is designed to improve the efficiency and quality of building, using, and maintaining knowledge bases.

**The Web Ontology Language (OWL)** is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

*conceptual diagram*

**Protégé** is a free, open source ontology editor and a knowledge management system.


**Shapes Constraint Language (SHACL)** is a World Wide Web Consortium (W3C) standard language for describing Resource Description Framework (RDF) graphs.

SHACL has been designed to enhance the semantic and technical interoperability layers of ontologies expressed as RDF graphs.
SHACL models are defined in terms of constraints on the content, structure and meaning of a graph. SHACL is a highly expressive language. Among others, it includes features to express conditions that constrain the number of values that a property may have, the type of such values, numeric ranges, string matching patterns, and logical combinations of such constraints. SHACL also includes an extension mechanism to express more complex conditions in languages such as SPARQL and JavaScript. SHACL Rules add inferencing capabilities to SHACL, allowing users to define what new statements can be inferred from existing (asserted) statements.

**Eclipse RDF4J (formerly OpenRDF Sesame)** is an open-source framework for storing, querying, and analysing RDF data.

RDF4J supports two query languages: SPARQL and SeRQL.