WT Semantic Web Fundamentalskti.tugraz.at/staff/vsabol/courses/wt/wt_slides_semweb.pdf · Vedran Sabol WT VU –Semantic Web Fundamentals December 2nd 2019 Semantic Web • ^A new

WT VU – Semantic Web Fundamentals January 7th 2019Vedran Sabol

Semantic Web Fundamentals

Web Technologies (706.704)

3SSt VU

WS 2018/19

Vedran Sabol

with acknowledgements to P. Höfler, V. Pammer, W. Kienreich

ISDS, TU Graz

Dec 2nd 2019

WT VU – Semantic Web Fundamentals December 2nd 2019Vedran Sabol

Overview

• What is Semantic Web?

• Technology stack

• Linked Data (Cloud)

• Example applications

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Semantic Web

• “A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities.”

Tim Berners-Lee, Scientific American, 2001

Also described as “Web 3.0” (where Web 2.0 is the social Web)

• Semantic Web is a web of data

Addition to the classic Web of documents

• Goals

Give Web information an exact meaning (semantics)

Provide common formats for data integration/combination

• enabled by “being about the same thing” (semantics)

Empower computers to understand, process and integrate Web information

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Semantic Web Stack

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Resource Identification

• Unicode: encoding standard for text, includes many character sets (covering different languages)

• Used for encoding resources in the Semantic Web

• defines UTF-8 (preferred), UTF-16, UTF-32 character encodings

• Current version: 12.1

137,994 characters

from 150 scripts (currently in use and historic)

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• URL: Uniform Resource Locator

Reference to a resource in the Web

Also defines the means for accessing the resource (e.g. http://)

• URN: Uniform Resource Name

Identifies a resource within a specific namespace (e.g. ISBN –International Standard Book Number)

• IRI/URI (Internationalized Resource Identifier/Uniform Resource Identifier): unique identification of resources

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• URI: Uniform Resource Identifier

Standard for identifying resources in the Semantic Web

Generalisation of URLs

Can be a “virtual” pointer (e.g. not associated to content/document)

Consist of

• Scheme name: e.g. “http:”

• Authority: e.g. “//myhost.com:8080”

• Path: e.g. “/dir/subdir/file.db”

• Query: e.g. “?date=20141201&place=graz”

• Fragment: e.g. “#foto”

• IRI: Internationalized Resource Identifier

Extends URIs from ASCII to Universal Character Set

• Best practice: HTTP URIs

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XML

• XML: meta-markup language

Used to define a syntax for creating documents containing structured data

Nested opening and closing tags define a hierarchy of elements

Attributes define element properties

Content belonging to an element stored between tags

• XML Namespaces: provide a possibility to use uniquely named elements and attributes from different sources (vocabularies)

Declared using reserved attribute xmlns:prefix=“namespaceURI”

• xmlns:xhtml="http://www.w3.org/1999/xhtml"

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Resource Description Framework (RDF)

• RDF is a framework for creating statements about resources in the form of “triples”

Triples are subject-predicate-object expressions

Graph representation of resource information

• RDF Schema (RDFS) provides basic vocabulary for RDF

Allows to define application-specific classes and properties

Resources defined as instances of classes and subclasses (like in OOP)

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Resource Description Framework (RDF)

• Language for representing information about Web resources

• Consists of triples:

<subject> <predicate> <object>

• Different serialisation formats - initially XML

• Designed to be understood by computers

Not indented for consumption by humans

• W3C recommendation since 2004

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RDF - Examples

In English

• Moby-Dick was written by Herman Melville.

In RDF

• <http://dbpedia.org/resource/Moby-Dick>

<http://dbpedia.org/ontology/author>

<http://dbpedia.org/resource/Herman_Melville>.

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In English

• Tim Berners-Lee likes Moby-Dick.

In RDF

• <http://www.w3.org/People/Berners-Lee/card#i>!

<http://www.w3.org/2000/10/swap/pim/contact#likes>

<http://dbpedia.org/resource/Moby-Dick>.

http://dbpedia.org/resource/Moby-Dick

http://dbpedia.org/ontology/author

http://dbpedia.org/resource/Herman_Melville

http://www.w3.org/People/Berners-Lee/card#i

http://www.w3.org/2000/10/swap/pim/contact#likes

http://dbpedia.org/resource/Moby-Dick


RDF - Graph

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Tim Berners-Lee

Person

Herman Melville

Moby Dick

Book

is a

is a

likes

author is a

published on

„1815-10-18“


RDF Serialization Formats

RDF/XML

<?xml version="1.0" encoding="utf-8" ?>!

<rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:dbo="http://dbpedia.org/ontology/">!

<rdf:Description rdf:about="http://dbpedia.org/resource/Moby-Dick">

<dbo:author rdf:resource="http://dbpedia.org/resource/Herman_Melville" />

</rdf:Description>

</rdf:RDF>

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RDF Serialization Formats

N3

@prefix dbo: <http://dbpedia.org/ontology/> .

<http://dbpedia.org/resource/Moby-Dick>

dbo:author <http://dbpedia.org/resource/Herman_Melville> .

JSON-LD

{

"@id": "http://dbpedia.org/resource/Moby-Dick",

"http://dbpedia.org/ontology/author": {

"@id": "http://dbpedia.org/resource/Herman_Melville"

}

}

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RDF Schema (RDFS) and Web Ontology Language (OWL)

• Data-modelling vocabulary with pre-defined semantics for RDF data

an RDFS document is a valid RDF document

• Contains elements for defining ontologies

Classes, properties, data types, sub-classing etc.

• Classes are sets of instances (rdfs:Class), e.g.

• rdfs:Resource (class of all URIs, all other classes are subclasses)

rdf:Property (class of all properties)

rdfs:Literal (class of all literals, e.g. strings or integers)

rdfs:Datatype (class of all datatypes)

rdfs:Container (super-class of rdf:Alt, rdf:Bag, rdf:Seq)

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RDF Schema (RDFS) and Web Ontology Language (OWL)

• OWL extends RDFS with more advanced constructs

• Extended vocabulary

• Stating additional constraints

cardinality, value restrictions, characteristics of properties (e.g. transitivity).

• Brings reasoning power to the semantic web

• Entity names are IRIs

• RDF defines how to write statements, OWL defines what is valid to write

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Ontology

• Definition: naming and definition of the types, properties, and relationships of entities in a particular domain

“a formalization of a conceptualization”

• Encoded using ontology languages (RDF, RDFS, OWL etc.)

• An ontology has an IRI

• And may

have a version number

import other ontologies

Be described using metadata (e.g. with RDFS)

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Ontology

Consist of

• Instances (basic objects)

• Classes (define different types of instances)

• Attributes (describe classes and instances)

• Relations (put classes and instances into relation)

• Restrictions (to something being accepted as input)

• Rules (if-then statements describing logical inference)

• Axioms (assertions/statements, including rules)

Declarations: non-logical axioms to ensure IRIs are used for proper entity types (e.g. Declaration(NamedIndividual(:Frenkie)), ClassAssertion(:Manager :Frankie))

Assertional Axioms: assert facts or annotations about entities

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Triple Stores

• Databases for storing RDF statements

• Optimized for storage of triples and the resulting graph structures

Related to graph DBs (which are more general)

May be implemented on top of existing DB engines

• Retrieval using semantic queries

Query execution efficiency is the hard part

• Examples: AllegroGraph, OpenLink Virtuoso, Jena…

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SPARQL Protocol and RDF Query Language (SPARQL)

• SPARQL is the RDF query language for a triple store

Interface is called SPARQL end point

• 4 query types

ASK: returns a true or false value

SELECT: matching resources returned in a table format

CONSTRUCT: returns results as valid RDF

DESCRIBE: returns RDF statements describing the matching resources

• it is up to the endpoint to decide which descriptions are included

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SPARQL Protocol and RDF Query Language (SPARQL)

• SPARQL is the RDF query language for a triple store

Interface is called SPARQL end point

• Query exampleSELECT ?book ?releaseDate

WHERE {

<http://www.w3.org/People/Berners-Lee/card#i> con:likes ?book .

?book dbpedia:releaseDate ?releaseDate .

}

• Result(<http://dbpedia.org/resource/Moby-Dick>, “1851-10-18”)

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Linked Data

“The Semantic Web isn't just about putting data on the web. It is about making links, so that a person or machine can explore the web of data. With linked data, when you have some of it, you can find other, related, data.”

— Tim Berners-Lee

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Linked Data

• Publishing and interlinking structured data on the Web

• Using Semantic Web standards

• Discoverable using SPARQL

• 4 principles (by Tim Berners-Lee)

Use URIs as names for things

Use HTTP URIs so that people can look up those names

When someone looks up an URI, provide useful information, using standards (RDF, SPARQL)

Include links to other URIs, so that they can discover more things.

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Linked Open Data (LOD)

• W3C SWEO (Semantic Web Education and Outreach) Community Project

“The goal of the W3C SWEO Linking Open Data community project is to extend the Web with a data commons by publishing various open data sets as RDF on the Web and by setting RDF links between data items from different data sources”

• Examples

DBpedia: data from Wikipedia, structured using semantic Web technologies

FOAF: dataset describing persons, their properties and relations

GeoNames: descriptions of geographical features worldwide

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https://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

https://en.wikipedia.org/wiki/DBpedia

https://en.wikipedia.org/wiki/FOAF_(software)

https://en.wikipedia.org/wiki/GeoNames


LOD Cloud

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Explore the LOD cloud (SVG)

https://lod-cloud.net/clouds/lod-cloud.svg


Ontology Alignment

Problem: LOD cloud is far from perfect

• different domains/view-points diversity of vocabularies and conceptualizations

• no links between them

Ontology alignment: establish links between (equal) concepts from different ontologies to enable interoperability

• Multitude of techniques: statistical, linguistic, structural, rule-based etc.

Ontology Alignment Algorithms

External Knowledge

(WordNet…)

Statistical Methods

LinguisticMethods

Knowledge Base

Ontology A

Ontology B Aligned Ontologies

matchmatch


UI Layer - Example

• UI Layer enables humans to use semantic Web applications

• Example application: Query Wizard + Visualization Wizard for LOD

Query Wizard: Search for datasets like you search for documents

Visualization Wizard: automatically generate interactive visualizations

Rely on semantic information

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Query Wizard

• Google-like search in Linked Data

• Keyword search + attribute selection (columns)

• Search result: a tabular result set

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Visualization Wizard

• 10+ different visualisations

time, geo-spatial, categorical, statistics…

• Automatic visualisation of tabular data sets

depending on data characteristics

and visualization capabilities

• Filtering and aggregation

• Interactive analysis with Multiple Coordinated Views

• Utilize Semantic Web technologies to achieve the goals

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Visualization Wizard - Ontologies

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• RDF Data Cube Vocabulary (W3C standard)

Represents statistical data as collections of observations

Dimensions: identify observations

Measures: are related to concrete values

• Visual Analytics Vocabulary

• Describes visualizations semantically as an OWL ontology

• Chart name

• Definition of

– visual channels of the visualization: available axes, colors, different icons, item size…

– And their data presentation capabilities: data type, cardinality, persistence


Visualization Wizard - Mapping

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• The two ontologies define the relation between data and visualizations

Compute all valid mappings of a data set onto visualizations

These visualizations (and only these) can be created automatically with a single click


Visualization Wizard UI

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Meaningful charts can be created automatically

• Bar Chart (selected)

• Pie Chart

• Parallel Coordinates

• Geo View

Other charts are disabled

• Cannot be created for that data set


Visualization Wizard – Selection/Filtering

• Multidimensional data

• Data elements - lines– Colours - property

(violet - EU countries)

• Exploration– Filter along multiple axes

(e.g. years, CO2 Tons/P)

– Read values on other axes

– Spot patterns, dependencies


Visualization Wizard – Coordinated Multiple Views

• Interactive analysis using coordinated brushing

Selection in one visualization (scatterplot) reflected in all others (geo)

• Visualising data sets in multiple visualisations

Each visualization specialized for a different data aspect

numerical values, categories, time and geo information…


Visualization Wizard - Aggregation

• Aggregate CO2 emissions and life expectancy– Average for countries over the years

• Correlation: CO2 emissions and life expectancy– Outliers: Russia, Saudi Arabia


Visualization Wizard – Aggregation

aggregate value (sum) for countries over the years

• Data transformations - such as aggregation - create new data sets!


Visualization Wizard – Coordinated Multiple Views

• Problem: how to link different data sets to enable interactive analysis in multiple visualizations?

• Linking enabled by semantic information (through “being about the same thing”)

for data sets created by data transformations

and for any data set loaded from the web

• Examples of insights obtained through semantic coordinated brushing

Countries with lowest funding lie in Eastern Europe

Their funding is increasing over the years – but so is for the other counters too

Documents

WT Semantic Web Fundamentalskti.tugraz.at/staff/vsabol/courses/wt/wt_slides_semweb.pdf · Vedran Sabol WT VU –Semantic Web Fundamentals December 2nd 2019 Semantic Web • ^A new