1. Web-Technology
Lecture 13

2. Semantic Web

3. Pre-quiz Who knows what Semantic Web is?
Who knows what R, D and F stand for in RDF?
Who knows why will we mention Dublin today?
Who knows what owl has to do any of this?

4. A Very General Motivation
Every language has its own syntax and semantics
Syntax is the study of grammar. It defines how to structure a message
how to say something?
Semantics is the study of meaning. It defines how to interpret a message
how to understand what one says?
Different syntaxes can have the same semantics
x += y
x = x + y

5. What does it have to do with WebTech?
Both syntax and semantics help communicate information
Web is the largest information system
The syntax of information on the Web is defined by…
Where is its Semantics?..
It does not have its own. By default, it did not need any.
WWW has been created to be consumed by humans.
We read and write in a natural language
we understand the texts and the purpose of properly placed links
Computers - not so much
HTML
My Therapist told me "Write letters to the people who you hate and burn them later". I did that, feel much better now... But what should I do with the letters???

6. Can we make computers understand the Web?
The Semantic Web (2001) By Tim Berners-Lee, James Hendler and Ora Lassila
Not the first piece about the term, but, probably, the most influential
Main idea:
Information on the Web can be given meaning
This will allow computers (agents) to understand it and communicate to each other based on this information
This will allow automate many online activities by giving computers complex tasks and delegating step-by-step execution of this tasks to them
A kind of a distributed Artificial Intelligence
Where would an old Web go?
Nowhere. Semantic Web is not a substitute or an update, but an extension

7. How does one build Semantic Web
He does not… not alone
Building semantic technologies involves lots of work and knowledge
SW is an evolution, not a revolution
It requires:
Many public data sets…
…provided with metadata…
…with values interlinked…
.. and elements defined by common ontologies…
…and represented using open standards

8. Semantic Web Technologies
Publisher
Title
With an example
Genre
Author

9. RDF RDF stands for RDF statements expressing knowledge are triples
Resource: pages, dogs, ideas...everything that can have a URI
Description: attributes, features, and relations of the resources
Framework: model, languages and syntaxes for these descriptions
RDF statements expressing knowledge are triples
every piece of knowledge is broken down into (subject : predicate : object)
e.g.: Jane-Eyre : has-an-author : Charlotte-Bronte
RDF is a graph model that links description of resources together into networks
Subjects and objects are nodes, predicates are links
has-a-publisher
has-an-author
Penguin books
Jane-Eyre
Charlotte-Bronte

10. RDF (cont.) Resources are identified by their URIs (IRIs)
Subject is an URI or a blank node
Predicate is an URI
Object is an URI, blank node or a literal
RDF models have unique namespaces (URIs of the models themselves)
URIs can be represented relatedly to a name space where they are defined
=> dc:creator
RDF specification defines the rules for creating RDF graphs and datasets as well as the basic RDF vocabulary (revised in 2014)

11. RDF-Schema (RDFS) RDF-Schema: extends RDF basic vocabulary
allows to define new properties (predicates) and classes (types of resources)
Provides basic means for defining and controlling the semantics of RDF models
rdf:type
rdfs:subClassOf
rdfs:subPropertyOf
rdfs:domain (possible subjects)
rdfs:range (possible objects)
“Jane Eyre”
my:has-title
my:has-publisher
my:has-author
rdf:type
my:Book
rdf:type
rdfs:subClassOf
my:Person
my:CreativeWork

12. Standard RDF Vocabularies
DC and DCterms (Dublic Core) – cataloguing digital resources
FOAF (Friend Of A Friend) - linking people on the Web
vCard – information about people and organisations
geo – geographical information …
“Jane Eyre”
dc:title
dc:publisher
dc:creator
rdf:type
my:Book
rdf:type
rdfs:subClassOf
foaf:Person
my:CreativeWork

13. Ontologies
In Philosophy: “Ontology is the study of being or existence. It seeks to describe the basic categories and relationships existing in reality.”
In AI and CS: “Formal ontology is an explicit intentional specification of shared conceptualization”
In Semantic Web: “Web-ontology is a document that formally defines relations among terms in a sharable format”.

14. Ontologies eat Lion Antelope ? Crocodile Lion Crocodile subClassOf
Carnivore
Animal
eat
subClassOf
not
subClassOf
Herbivore
Plant
eat
Tree
subClassOf

15. Web Ontology Language (OWL)
OWL(2) is the main language for ontologies on SW
It is built on top of RDF, RDF Schema and RFF/XML syntax
Helps to define:
Class and property hierarchies
Instances
Axioms / constraints
Based on formal description logic, which means:
Proper OWL ontology does not have logical conflicts
New knowledge can be safely derived through formal inference and querying the ontology

16. Reasoning and Inference
Lion
OWL(RDF) models are knowledge bases
Some knowledge is specified
New knowledge can be inferred
Do Lyons eat Antelopes?
Do Lyons eat Crocodiles?
Do Crocodiles eat Crocodiles?
Do Lyons eat Trees?
Do Antelopes eat Grass?
Crocodile
subClassOf
subClassOf
Antelope
subClassOf
Carnivore
Animal
eat
subClassOf
not
subClassOf
Herbivore
Plant
eat
Tree
subClassOf
subClassOf
Inference Engines
Grass
Description Logic
Semantic Web Rule Language (SWRL)
hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3)

17. Querying RDF models are data sets
SPARQL - SPARQL Protocol and RDF Query Language
retrieve and manipulate data from RDF models and data sets
Similar in functionality to SQL
PREFIX ex:
SELECT ?capital ?country
WHERE {
?x ex:cityname ?capital ;
ex:isCapitalOf ?y .
?y ex:countryname ?country ;
ex:isInContinent ex:Africa . }

18. Criticism of Semantic Web
Practical feasibility
HTML is easy
Semantic Web is complicated
Censorship and privacy
Semantic layer of Web information makes it easier for governments to discover knowledge and control
Doubling output formats
Information has to be presented in a regular and a semantic way

19. The current state of Semantic Web

20. The current state of Semantic Web

21. Open Linked Data Big data is big Most of it exists is silos
18 mil. open data sets
This is more than web-pages when Google was founded
Most of it exists is silos
Isolated databases
What if all of it can be
Shared
Externalized
Connected

22. DB Pedia Semantic snapshot of Wikipedia
more than 3.5 million things represented
almost 2 million are classified in a consistent ontology
2,7 mil. links to images and 6,3 mil. links to external web pages
over 1 billion pieces of information (RDF triples)

23. Linked Data Deployment

24. Ontological Agreement

25. RDF Links

26. HTML-embedded Data

27. Schema.org Work started in August 2010
Google, Yahoo!, Microsoft & then Yandex
Goals:
One vocabulary understood by all the search engines
Make it very easy for the webmaster
It is A vocabulary. Not The vocabulary.
Webmasters can use it together other vocabularies
We might not understand the other vocabularies. Others might

28. Principles Incremental effort Simplicity
Simple things should be simple
Webmasters shouldn’t have to deal with N namespaces
Complex things should be possible
Advanced webmasters should be able to mix and match vocabularies
has to fit in with existing workflows
Incremental effort
Started simple ( ~ 100 categories at launch)
Applies to every area
Add complexity after adoption (now >1200 vocab items)
Go back and fill in the blanks
Collaboration
Partner with Authoring platforms (Drupal, Wordpress, Blogger, YouTube…)
Work with others to incorporate their vocabularies
Any syntax possible (Microformats, RDFa, JSON-LD, …)

29. Overall Adoption in 2015

30. Widely-used Classes

31. Adoption by E-Commerce Websites

32. Properties used to Describe Products

33. Adoption by Travel Websites

34. Properties used to Describe Hotels

35. Adoption by Job Portals

36. Properties used to Describe Job Postings

37. Google’s Knowledge Graph
Launched in 2012
Global network of entities (not words)
associated data
links between relevant entities
enhanced with historical search data
Rich search snippets
Relevant links to explore
Facilitates inquiry
Google’s Knowledge Graph

38. Facebook’s Open Graph

39. Personal Assistants
Siri’s knowledge is represented in a unified modeling system that combines ontologies, inference networks, pattern matching agents, dictionaries, and dialog models. ... Siri isn’t a source of data, so it doesn’t expose data using Semantic Web standards

40. Post-quiz Who knows what Semantic Web is?
Who knows what R, D and F stand for in RDF?
Who knows why have we mentioned Dublin today?
Who knows what owl has to do any of this?