The document presents an overview of the Semantic Web, emphasizing its purpose of enhancing data sharing and search processes by using machine-readable semantics. It contrasts the 'dumb web,' primarily designed for human interaction, with the 'smart web,' which enables easier machine communication through structured data and linked data models. Key concepts discussed include RDF, OWL, and data integration frameworks that facilitate more efficient access and organization of information across various domains.

1. Semantic Web
PRESENTED BY: ANDRÉ MAZAYEV
ANDRE.MAZ.90[ AT]GMAIL[DOT]COM

2. Semantic Web? Whaaat?
◦ What semantic web means?
◦ Smarter web!! Duuuh!

3. Semantic Web? Whaaat?
◦ What semantic web means?
◦ Smarter web!! Duuuh!
◦ Ok. But more specifically?
◦ It’s a web where it is easier to find stuff on internet

4. Semantic Web? Whaaat?
◦ What semantic web means?
◦ Smarter web!! Duuuh!
◦ Ok. But more specifically?
◦ It’s a web where it is easier to find stuff on internet
◦ Yeah! But how?
◦ Hmmmmm……

5. Web 2.0
◦ Search Process
◦ Refine search as you go
◦ The user is guiding the search accordingly to the results that are shown
◦ Search engine is only performing syntax based pattern match
◦ Plus some features to improve performance and accuracy
◦ Semantics are not used or used in a limited way during the search process

6. Syntax and Semantics
◦ Syntax
◦ About form
◦ Semantics
◦ About meaning

7. Syntax and Semantics
◦ Syntax
◦ Green, Yellow, Red
◦ Semantics
◦ Green = Go
◦ Yellow = Better stop
◦ Red = Stop
Traffic Light
Adapted from: Semantic Web from the 2013 Perspective

8. User’s Web Example
Example of dumb web
◦ Goal
◦ Find the telephone number of James Bond

9. User’s Web Example
Example of dumb web
◦ Goal
◦ Find the telephone number of James Bond
◦ For humans the answer is easy to find
◦ James Bond’s telephone number is 1-800-555-0199
◦ James Bond is a fictional MI6 agent
◦ Since it’s a fictional agent we can infer that the number must be fake

10. Machine’s Web Example
Example of dumb web
Source code of dumb web
◦ For machines find Bond’s number is a hard task
◦ No machine “readable” semantics
◦ Current Web
◦ Created for document sharing
◦ Instead of data sharing
◦ Adapted for Human to Human
◦ Machine to Machine communication is difficult

11. Smart vs Dumb Web
Example of dumb web
Example of smart web

12. Smart vs Dumb Web
Visually both pages are identical
Smart page carries much more
“meaning”
Example of dumb web
Example of smart web

13. Smart vs Dumb Web
Source code of smart webSource code of dumb web

14. Source code analysis
Contains more machine friendly structure
◦ Vocabulary is defined
◦ Data is structured
◦ Data is enriched
The data can be represented as a graph
Source code of smart web

15. James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Source code analysis
Source code of smart web

16. James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Source code analysis
With structured data it’s easy for a machine to find Bond’s telephone number
Source code of smart web

17. Graph analysis
◦ Simple statements
◦ Subject – Predicate – Object
◦ All elements have their own URL
◦ Data is structured
◦ Data can be explored by machines
James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
URL
URL
URL

18. Structured Data Tool
Source code of a page with semantic markup Extracted data

19. Structured Data Tool
Extracted data
◦ Data recognized by Google’s web crawler
◦ With structured data answers are easy to get
◦ What?
◦ Where?
◦ When Open?

20. Semantic Web
Present Future
Web of Documents Web of Data
Small Change
Big Difference
◦ Data is explicit
◦ Data is connected
◦ Data can be explored by machines
◦ Nontrivial connections can be found
◦ Demo
◦ RelFinder

21. Semantic Building Blocks

22. RDF

23. RDF
◦ Resource Description Framework
◦ Simple statements (triples)
◦ Subject – Predicate – Object
◦ Building block of RDFS and OWL
◦ Multiple serialization formats
◦ RDF/XML
◦ Turtle
◦ N-Triples
Bond example in Turtle

24. RDFS

25. RDFS
◦ RDF Schema
◦ Limited expressivity
◦ Describes classes, subclasses and properties
◦ Primary focused on “is a” and “sub class of”
relationships
Vocabulary
Canine
Animal Human
Mammal
Feline
Reptile
Taxonomy
Animal
MammalReptile
Human
Canine Feline
subClassOf subClassOf
subClassOf subClassOf
subClassOf

26. SPARQL

27. SPARQL
◦ SPARQL Protocol And RDF Query Language
◦ SQL-Like structure
James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Graph

28. SPARQL
◦ SPARQL Protocol And RDF Query Language
◦ SQL-Like structure
James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Graph
Goal: Find Bond’s Number

29. SPARQL
◦ SPARQL Protocol And RDF Query Language
◦ SQL-Like structure
James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Graph
Query
Goal: Find Bond’s Number

30. SPARQL
◦ SPARQL Protocol And RDF Query Language
◦ SQL-Like structure
James
Bond
1-800-555-0199
James Bond
typeof
name
telephone
Person
Graph
Answer
Query
Goal: Find Bond’s Number

31. OWL

32. OWL
◦ Web Ontology Language
◦ Highly expressive
◦ Brings expressivity of logic to Semantic Web
◦ More expressive than RDFS
◦ Allows to express
◦ Constraints
◦ Cardinality
◦ Unions
◦ Intersections
◦ Etc.
Resource that has property hasParent with value
Bond belongs to a class named BondChild
OWL Restriction
Note: Often the concepts of taxonomies and ontologies overlap and used to describe same thing

33. SWRL

34. SWRL
◦ Semantic Web Rule Language
◦ Combines parts from OWL and Datalog
◦ Rule syntax
◦ If body (antecedent) then assert head (consequent)
x3 is x1’s uncle

35. Under Development
◦ Pending questions
◦ How to ensure security of data?
◦ How to validate new data?
◦ Is source data reliable?

36. Data Silos
◦ Each application has its own
◦ Goals
◦ Vocabularies
◦ Knowledge base
◦ Not integrated with other data systems
◦ May have overlapping data
Application 1
Application 2
Application 3
Sensor
Network
Gateway
Server Application
Data
Source
Relational
DB
Relational
DB

37. Semantic Bridges
Sensor
Network
Gateway
Server
Data
Source
Relational
DB
Relational
DB
RDB
Parser
CSV
Parser
WEB
Parser
RDF
Interfaces
Combined RDF
Model
Combined
Knowledge Model
Application 1
Application 2
Application 3
Application
RDB
Parser
CSV
Parser
WEB
Parser
Model Knowledge Model
Application 1
Application 2
Application 3
er
RDB
Parser
CSV
Parser
WEB
Parser
RDF
Interfaces
Combined RDF
Model
Combined
KnowledgeModel
Application 1
Application 2
Application 3
Application
CSV
Parser
WEB
Parser
Application 2
Application 3

38. Semantic Bridges
Sensor
Network
Gateway
Server
Data
Source
Relational
DB
Relational
DB
RDB
Parser
CSV
Parser
WEB
Parser
RDF
Interfaces
Combined RDF
Model
Combined
Knowledge Model
Zoom
Application 1
Application 2
Application 3
Application
RDB
Parser
CSV
Parser
WEB
Parser
Model Knowledge Model
Application 1
Application 2
Application 3
er
RDB
Parser
CSV
Parser
WEB
Parser
RDF
Interfaces
Combined RDF
Model
Combined
KnowledgeModel
Application 1
Application 2
Application 3
Application
CSV
Parser
WEB
Parser
Application 2
Application 3

39. Data Integration
Data Sets
Combined RDF
Model
Combined
Knowledge Model
◦ Data from different sources is
combined into a common model
◦ The whole is greater than the sum
of its parts
◦ New knowledge can be obtained

40. Data Integration
Animal
MammalReptile
Human
Canine Feline
subClassOf subClassOf
subClassOf subClassOf
subClassOf
Wolves Terriers
Hounds
subClassOf
subClassOf
subClassOf
Foundation
Ontology
Extended
Ontology
◦ Foundation ontologies transcend
boundaries of single knowledge domain
◦ Common environment for
◦ Different terminologies
◦ Different knowledge domains
◦ Makes data integration easier
◦ Can be done (semi) automatically
◦ Easier to obtain new knowledge

41. M3 Framework
◦ Four data sources
◦ Different domains
◦ Overlapping data
◦ Same vocabulary
◦ Combined knowledge model
Adapted from: Machine-to-Machine Measurement (M3) Framework

42. M3 Framework
◦ Smart Band sends a set of
measurements about user
◦ One of the measurements is
body temperature
Adapted from: Machine-to-Machine Measurement (M3) Framework

43. M3 Graph View
Adapted from: Machine-to-Machine Measurement (M3) Framework

44. M3 Framework
◦ Naturopathy expert describes
lemon and it’s properties
◦ Lemon is good to treat cold
Adapted from: Machine-to-Machine Measurement (M3) Framework

45. M3 Graph View
Adapted from: Machine-to-Machine Measurement (M3) Framework

46. M3 Framework
◦ Doctor describes High Fever as
symptom of Cold
◦ Given
◦ Doctor’s info
◦ Lemon’s properties
◦ Framework can infer that
◦ Lemon is good to treat High Fever
Adapted from: Machine-to-Machine Measurement (M3) Framework

47. M3 Graph View
Adapted from: Machine-to-Machine Measurement (M3) Framework

48. M3 Framework
◦ User creates a rule:
◦ If body temperature is higher than 38
◦ Then user has High Fever
◦ Given
◦ Sensor measurement
◦ User’s rule
◦ Doctor’s info
◦ Framework can infer that
◦ User has Cold
Adapted from: Machine-to-Machine Measurement (M3) Framework

49. M3 Graph View
Adapted from: Machine-to-Machine Measurement (M3) Framework

50. M3 Framework
◦ Given all the data
◦ Framework can recommend to
the user a lemon tea to treat the
cold
Adapted from: Machine-to-Machine Measurement (M3) Framework

51. M3 Graph View
Adapted from: Machine-to-Machine Measurement (M3) Framework

52. Linked Open World
◦ Linked Open Data
◦ Data repositories (DataHub, Data.gov, etc.)
◦ Share data to generate new data
◦ Linked Open Vocabularies
◦ Vocabularies repositories
◦ Facilitates data integration
◦ Linked Open Rules
◦ Rules repositories
◦ Concept only
◦ Linked Open Services
◦ Service repositories
◦ Concept only

53. Thanks for your attention