Semantic Web from the 2013 Perspective

2nd MakoLab Semantic Day – Theoria and Praxis Polish Academy of Sciences, October 3rd, 2013

Prof. Dr. Adrian Paschke

Department of Information Systems

Poznan University of Economics and Freie Universitaet Berlin

paschke@inf.fu-berlin

Prof. Dr. Witold Abramowicz

Department of Information Systems

Poznan University of Economics http://kie.ue.poznan.pl/en

Scientific Center of the Polish Academy of Sciences in Paris

Poznan University of Economics

specialises in educating economists,

managers and specialists in quality

management in all sectors of the

economy

Research labs

Enterprise platforms and systems

Service science

Next Generation Internet

Semantic as a leitmotif

Semantic related EU projects

SUPER – Semantics Utilised for Process Management within and between Enterprises

ASG – Adaptive Services Grid

INSEMTIVES – Incentives for Semantics

EASTWEB: building an integrated leading Euro-Asian higher education and research community in the field of the Semantic

USE-ME.GOV - Usability driven open platform for mobile government

T-OWL – Time-determined Ontology based knowledge system for real time stock market analysis

Service Web 3.0

ENIRAF - Enhanced Information Retrieval and Filtering for Analytical Systems

KnowledgeWeb

Other semantic related projects

eDW – enhanced Data Warehouse

eVEREst – The System to Support Government’s Estimation of Real Estates’ Value

F-WebS – Filtering of Web services – semantic description of Web services

Adaptive microWorkflow – Acquisition and Filtering of Information for the Needs of Adaptive microWorkflows

EGO – Identity management

Semiramida – ontological representation of legal acts

Integror-S3 – Semantically-Enhanced Execution Engine

eXtraSpec – Advanced data extraction methods for the needs of expert search

ASBK – Adaptive Systems for Corporate Banking

FEMS – Future Energy Management System

DWDI – Deep Web Data Integration

Agenda

What is Semantics?

The Semantic Web – An

Introduction

Semantic Web and it’s Relations

What comes next?

What is Semantics?

Search Results from Publication Database

Lorenz P, Transcriptional repression mediated by the KRAB domain of the human C2H2 zinc finger protein Kox1/ZNF10 does not require histone deacetylation. Biol Chem. 2001 Apr;382(4):637-44.

Fredericks WJ. An engineered PAX3-KRAB transcriptional repressor inhibits the malignant phenotype of alveolar rhabdomyosarcoma cells harboring the endogenous PAX3-FKHR oncogene. Mol Cell Biol. 2000 Jul;20(14):5019-31. ...

Author

Title

Year Journal

However, for a machine things look different!

Results from Publication Database

Lorenz P, Transcriptional repression

mediated by the KRAB domain of the

human C2H2 zinc finger protein

Kox1/ZNF10 does not require histone

deacetylation.

Biol Chem. 2001 Apr;382(4):637-44.

Fredericks WJ. An engineered PAX3-

KRAB transcriptional repressor inhibits

the malignant phenotype of alveolar

rhabdomyosarcoma cells harboring the

endogenous PAX3-FKHR oncogene.

Mol Cell Biol. 2000

Jul;20(14):5019-31.

Solution:

Tags (XML)?

Results from Publication Database

<author>Lorenz P</author><title>Transcriptional repression

mediated by the KRAB domain of the human C2H2 zinc finger

protein Kox1/ZNF10 does not require histone deacetylation.

</title>

<journal>Biol Chem </journal><year>2001<year>

<author>Lorenz P</author><title>Transcriptional repression

mediated by the KRAB domain of the human C2H2 zinc finger

protein Kox1/ZNF10 does not require histone deacetylation.

</title>

<journal>Biol Chem </journal><year>2001<year>

... However, for a machine things look different!

Results from Publication Database

<author>Lorenz

P</author><title>Transcriptional

repression mediated by the KRAB

domain of the human C2H2 zinc finger

protein Kox1/ZNF10 does not require

histone deacetylation. </title>

<journal>Biol Chem

</journal><year>2001<year>

<author>Lorenz

P</author><title>Transcriptional

repression mediated by the KRAB

domain of the human C2H2 zinc finger

protein Kox1/ZNF10 does not require

histone deacetylation. </title>

<journal>Biol Chem

</journal><year>2001<year>

Solution: Use Semantic

Knowledge

Example: Traffic Light Syntax – Semantics - Pragmatics

Syntax

green (bottom); yellow; red

Semantics

green = go; …; red = stop

Pragmatics

If red and no traffic

then allowed to go

Example: Question-Answer Interaction Syntax – Semantics - Pragmatics

Syntax

“What time is it?” (English)

Semantics

Question about current time (Meaning)

Pragmatics

An answer to the question is obligatory

(even if time is unknown) (Understanding

and Commitment)

Example - XML Syntax vs. Semantics

Adrian Paschke is a lecturer of Logic Programming

<course name=“Logic Programming">

<lecturer>Adrian Paschke</lecturer>

</course>

<lecturer name=“Adrian Paschke">

<teaches>Logic Programming</teaches>

</lecturer>

Opposite nesting (syntax), same meaning (semantics)!

Syntax

about form

Semantics

about meaning

Pragmatics

about use.

Syntax – Semantics - Pragmatics

Semantic Technologies for Declarative Knowledge Representation

1. Rules Describe derived conclusions

and reactions from given information (inference)

2. Ontologies Ontologies described the conceptual

knowledge of a domain (concept semantics) Partner

Customer

is a

equal with

Client

if premium(Customer)

then discount(10%)

Example: Ontology and Rules

Object

Person Document Topic

Patentee

Patent Application Patent

becomes

knows described_in

is_a-1

is_a-1

is_a-1

is_a-1

is_a-1

writes

related_to

Skill

has related_to

Topic Document Topic Document

Patent Application

Topic Patentee Topic

described_in

is_about knows

is_about

Patentee writes

RULES:

Patentee Skill has

granted Technique Teaching

described_in

Priority date

Prior Art

Ontology

Main Requirements of a Logic-based Ontology / Rule Language in IT

a well-defined syntax

a formal semantics

efficient reasoning support

sufficient expressive power

convenience/adequacy of expression syntax

The Semantic Web

An Introduction

Semantic Web – An Introduction

"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." Tim Berners-Lee, James Hendler,

Ora Lassila, The Semantic Web

„Make the Web understandable for machines“

W3C Stack 2007

Main Building Blocks of the Semantic Web

1. Explicit Metadata on the WWW

2. Ontologies

3. Rule Logic and Inference

4. Semantic Tools ,Semantic Web Services,

Software Agents

The (current) W3C Semantic Web Stack

W3C Semantic Web Stack since 2007

Ontologies

Rules

Semantic Web Information Model

RDF Query Language

Standard Internet Technologies

Overview on the Semantic Web Technologies

URI/IRI: Web Resource Identifiers

RDF

RDF as Web data model for facts and metadata

RDF schema (RDFS) as simple ontology language

(mainly taxonomies)

SPARQL as a RDF query language

Linked Data – data publishing method

Ontology

Expressive ontology languages

Web Ontology Language (OWL)

Overview on the Semantic Web Technologies (2)

Rules / Logic Extension of the ontology languages, e.g. with rules

Rule Interchange Format (RIF, RuleML)

Proof Generation of proofs-, interchange of proofs, validation

Trust Digital signatures

recommendations, ratings

Semantic Web Applications & Interfaces e.g. Semantic Search, Semantic Agents, …

W3C Semantic Web (state: 2013)

IRIs + CURIE (Compact URI)

RDF 1.1, HTML+RDFa 1.1, RDB2RDF

SPARQL 1.1

RIF 1.0 (second edition)

OWL 2.0 (second edition)

Linked Open Data

RDF 1.1, Turtel, JSON-LD 1.1, …

Provenance

Prov-DM, Prov-N, Prov-O, …

Linked Open Data Cloud

Unifying Logic

W3C Semantic Web Stack since 2007

• Not standardized in W3C Semantic Web stack yet

• Which semantics? (e.g., Description Logics, F-Logic, Horn Logic, Common

Logic,…)

• Which assumptions? (e.g., Closed World, Open World, Unique Name, …)

• …

Proof and Trust • Proof Markup Languages, Justifications and Argumentations, Provenance

Proofs • Claims can be verified, if there are evidences from other (trusted) Internet

sources • Semantic Reputation Models • …

Use Cases / Applications / Tools Application Programming Interfaces

Semantic-enriched Search

Content management

Knowledge management

Business intelligence

Collaborative user interfaces

Sensor-based services

Linking virtual communities

Grid infrastructure

Semantic Multimedia data management

Semantic Web Services

etc. see e.g.SWEO’s use case collection http://www.w3.org/2001/sw/sweo/public/UseCases/

More about applications and use cases this afternoon…

The Semantic Web

and it‘s relations

Other Semantic Standards/Specifications

ISO/IEC JTC 1/SC 32

ISO/IEC 11179

Metadata

Registries

Metadata Registry

Terminology Thesaurus Taxonomy

Data

Standards

Ontology

Structured

Metadata

Terminology

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Rose”,

“ClipArt

Rose”

ISO TC 37

Semantic

Web

W3C

Modeling

MOF

ODM

PRR

SBVR

API4KB

OntoIOP

OMG

Node

Node

Edge

Subject

Predicate

Object

Graph RDF(S) / OWL

SPARQL,RIF

Logic

Common

Logic

Prolog

ISO,

RuleML,…

FOL

RuleML

F-Logic

Metadata

Ontology Definition Metamodel

ODM brings together the communities (SE+KR) by providing:

Broad interoperation within Model Driven Architecture

MDA tool access to ontology based reasoning capability

UML notation for ontologies and ontological interpretation of UML

M2

M1

M3 MOF XMI

Of UML

UML XMI

Of User Model

MOF

UML

M0 User

Instances

User

Ontology

User

UML Model

MOF XMI

Of ODM

ODM Ontology XMI

Of User Model

ISO Topic Maps

ISO CL

W3C RDFS

W3C OWL

UML 2 (+OCL)

Example: OMG Ontology Definition Metamodel (ODM)

Example: Rule Markup Language Standards (RuleML)

RuleML 1.0 (Deliberation, Reaction, Defeasible, Modal, …)

Semantic Web Rule Language (SWRL)

Uses RuleML Version 0.89

Semantic Web Services Language (SWSL)

Uses RuleML Version 0.89

W3C Rule Interchange Format (RIF)

Uses RuleML Version 0.91 with frames and slots

OASIS LegalRuleML

Uses RuleML Version 1.0

OMG Production Rules Representation (PRR)

Input from RuleML

OMG Application Programming Interfaces four KBs (API4KB)

Input from Reaction RuleML 1.0

Social Semantic Web

The concept of the Social Semantic Web

subsumes developments in which social

interactions on the Web lead to the creation

of explicit and semantically rich knowledge

representations. (Wikipedia)

Corporate Semantic Web

Corporate Semantic Web (CSW) address

the applications of Semantic Web

technologies and Knowledge Management

methodologies in corporate environments

(semantic enterprises).

(www.corporate-semantic-web.de)

Corporate Semantic Web

Corporate Semantic Web

Corporate Semantic

Engineering

Corporate Semantic

Search

Corporate Semantic

Collaboration

Public Semantic Web

Corporate Business Information Systems

Business Context

Pragmatic Web

The Pragmatic Web consists of the tools,

practices and theories describing why and how

people use information. In contrast to the

Syntactic Web and Semantic Web the Pragmatic

Web is not only about form or meaning of

information, but about interaction which brings

about e.g. understanding or commitments.

(www.pragmaticweb.info)

What comes next?

Challenges for the Semantic Web

Syntax

Sematics

Pragmatics

Data Understanding

Connectedness

Information / Content

Knowledge

Intelligence / Wisdom

Understanding relations

Understanding patterns

understanding principles

Ontologies (Logic)

Rules (Logic)

??? (Human Logic + Machine Logic)

Pragmatic Web

Ubiquitous Open Web Platform for the Pragmatic Web 4.0

Monolithic

Systems Era

Desktop Computing

Desktop

World Wide Web 1.0

Connects Information

Syntactic Web

Semantic Web 2.0 Connects Knowledge

Social Semantic Web 3.0,

Web of Services & Things,

Corporate Semantic Web Connects

People, Services and Things

Ubiquitous Pragmatic Web 4.0 Connects Intelligent Agents and Smart Things

Semantic Web

Ubiquitous autonomic

Smart Services and

Things

Pragmatic Agent

Ecosystems

Ma

ch

ine

Un

ders

tan

din

g

Ubiquitous Next Generation Agents and Social Connections

Syntactic Web

Semantic Web

Pragmatic Web

HT

ML

XM

L

RD

F

Sm

art

A

ge

nts

Co

nte

nt

Pro

du

ce

r

Passive Active

Co

nsu

me

r

Smart Content

Smart Content

Smart Web TV

Massive

Multi-player Web Gaming

Situation Aware Real-time Semantic

Complex Event Processing

W3C Open Web Platform

Thank you …

Questions?

AG Corporate Semantic Web, FU Berlin paschke@inf.fu-berlin

http://www.inf.fu-berlin/groups/ag-csw/

http://www.corporate-semantic-web.de http://www.pragmaticweb.info