Semantic Web

PRESENTED BY: ANDRÉ MAZAYEVA N D R E . M A Z . 9 0 [ AT ] G M A I L [ D O T ] C O M

Semantic Web? Whaaat?◦ What semantic web means?

◦ Smarter web!! Duuuh!

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

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……

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

Syntax and Semantics◦ Syntax

◦ About form

◦ Semantics◦ About meaning

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

User’s Web Example

Example of dumb web

◦ Goal◦ Find the telephone number of James Bond

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

Machine’s Web Example

Example of dumb webSource 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

Smart vs Dumb Web

Example of dumb web

Example of smart web

Smart vs Dumb WebVisually both pages are identical

Smart page carries much more “meaning”

Example of dumb web

Example of smart web

Smart vs Dumb WebSource code of smart webSource code of dumb web

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

James

Bond

1-800-555-0199

James Bond

typeof

name

telephone

Person

Source code analysisSource code of smart web

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

Graph analysis

◦ Simple statements◦ Subject – Predicate – Object

◦ All elements have their own URL

◦ Data is structured

◦ Data can be explored by machinesJames

Bond

1-800-555-0199

James Bond

typeof

name

telephone

Person

URL

URL

URL

Structured Data Tool

Source code of a page with semantic markup Extracted data

Structured Data Tool

Extracted data

◦ Data recognized by Google’s web crawler

◦ With structured data answers are easy to get ◦ What?

◦ Where?

◦ When Open?

Semantic WebPresent 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

Semantic Building Blocks

RDF

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

RDFS

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

SPARQL

SPARQL◦ SPARQL Protocol And RDF Query Language

◦ SQL-Like structure

James

Bond

1-800-555-0199

James Bond

typeof

name

telephone

Person

Graph

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

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

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

OWL

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

SWRL

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

Under Development◦ Pending questions

◦ How to ensure security of data?

◦ How to validate new data?

◦ Is source data reliable?

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 ApplicationData

Source

Relational

DB

Relational

DB

Semantic Bridges

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Semantic Bridges

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Zoom

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

Sensor

Network

Gateway

ServerData

Source

Relational

DB

Relational

DB

RDB

Parser

CSV

Parser

WEB

Parser

RDF

InterfacesCombined RDF

Model

Combined

Knowledge Model

Application 1

Application 2

Application 3

Application

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

Data IntegrationAnimal

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

M3 Framework◦ Four data sources

◦ Different domains

◦ Overlapping data

◦ Same vocabulary

◦ Combined knowledge model

Adapted from: Machine-to-Machine Measurement (M3) Framework

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

M3 Graph View

Adapted from: Machine-to-Machine Measurement (M3) Framework

M3 Framework◦ Naturopathy expert describes

lemon and it’s properties

◦ Lemon is good to treat cold

Adapted from: Machine-to-Machine Measurement (M3) Framework

M3 Graph View

Adapted from: Machine-to-Machine Measurement (M3) Framework

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

M3 Graph View

Adapted from: Machine-to-Machine Measurement (M3) Framework

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

M3 Graph View

Adapted from: Machine-to-Machine Measurement (M3) Framework

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

M3 Graph View

Adapted from: Machine-to-Machine Measurement (M3) Framework

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

Thanks for your attention