AG Corporate Semantic Web

Freie Universität Berlin

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

Corporate Semantic Web

Prof. Dr. rer. nat. Adrian Paschke, Freie Universität Berlin, Corporate Semantic Web

SemTech Conference, 6-7. February 2012, Berlin, Germany

2

Agenda

• About Corporate Semantic Web

• Corporate Semantic Engineer

• Corporate Semantic Search

• Corporate Semantic Collaboration

• Summary and Future

3

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“

4

Semantic Technologies

1. Rules

• Describe conclusions and reactions from given information (inference)

• Declarative knowledge representation: “express what is valid, the responsibility to interpret this and to decide on how to do it is delegated to an interpreter / reasoner”

2. Ontologies

• Ontologies described the common knowledge of a domain (semantics):

• “An ontology is an explicit specification of a conceptualization “ T. Gruber

Semantics interoperability between (connected) vocabularies

5

About Corporate Semantic Web

1. Application of Semantic Web technologies in enterprise information systems (Semantic Enterprise)

• Collaborative workflows and (business) process management (e.g. e-Science workflows, Semantic Business Process Management)

• Knowledge Management (e.g. Semantic Knowledge Management, Semantic Corporate Memory)

2. Corporate = Business Context

• Application of Semantic Web technologies under economical considerations and business conditions (e.g. cost models, return on investment)

6

Corporate Semantic Web for Semantic Enterprises

Corporate Semantic Web

•Semantic Applications •Semantic Knowledge

•Semantic Content

Front Office

Back Office

Customer Portals

Call Center E-Commerce

CRM

SCM

CSCW DBMS BPM ITSM

ERP

SRM

7

Challenges for the Corporate Semantic Web

Syntax

Sematics

Pragmatics

Data Understanding

Connectedness

Information / Content

Knowledge

Intelligence / Wisdom

Understanding relations

Understanding patterns

understanding principles

8

Semantic Content (Semantic Data)

1. Automatic extraction of semantic from non-semantic data

• Linked Data Extraction

• Ontology Learning

2. (New) Semantic Data and Knowledge Engineering and Development

• Manual (e.g. semantic text editor, semantic Wiki, semantic CMS, ontology-/rule-engineering)

• Automated (e.g., user activity mining, text analysis)

9

Semantic Knowledge

Semantic Knowledge Management and “Semantic Organizational Memory"

• Relevant knowledge

• e.g. reuse of knowledge, faster search, faster knowledge transfer, efficient processes, etc.

• Semantic archives and knowledge repositories

• e.g. Linked Data, knowledge clouds, semantic Wikis, semantic knowledge bases such as triplestores, semantic personal CMS, etc.

• Semantic integration of data from different heterogeneous sources of corporate knowledge

• Analysis of the semantic data, in order to detect implicit knowledge and semantically represent it

10

Semantic Applications (Semantic Intelligence)

Semantic applications for

• Corporate Semantic Engineering

• Methods and tools for the management of corporate information and processes

• Support for the development of semantic enterprise solutions and products/services

• Semantic Corporate Search

• Solutions for semantic search in information repositories

• Semantic Corporate Collaboration

• New semantic collaboration platforms with which information, processes and knowledge can be collaboratively share, used and managed

11

• Learning and Training

• Decision makers and employees

• Economic considerations,

• i.e. business context

• Estimation of costs and benefits

• Development and usage of new Corporate Semantic Web technologies

• Incentives for adoption and use of semantic technologies

Pragmatics

12

Corporate Semantic Web

Corporate Semantic Web

Corporate Semantic

Engineering

Corporate Semantic

Search

Corporate Semantic

Collaboration

Public Semantic Web

Corporate Business Information Systems

Business Context

www.corporate-semantic-web.de

13

Domains of the Corporate Semantic Web

• Corporate Semantic Engineering

• Methods and tools for the precise, high-quality and economical development and management of ontologies and rule bases for business information and processes

• Semantic support for the software and process engineering

• Semantic Corporate Search

• Solutions for the semantic search in controlled information resources with defined quality of service improvements

• Semantic Corporate Collaboration

• New semantic collaboration and support platforms with which different enterprise domains or parts of virtual organizations can collaboratively collect, use and manage information, processes / services and knowledge

14

• Ontology modularization and integration

• Ontology versioning

• Ontology cost estimation models for corporation

• Ontology evaluation

Corporate Semantic Engineering

Corporate Semantic

Engineering

15

Example: Corporate Ontologies

• Ontology supported Semantic Knowledge

• Semantic Bridges between Heterogeneous Information Systems

• Asynchronous evolution of the stand-alone systems and underlying corporate (background) knowledge

Corporate Wikis

Corporate Blogs Corporate Websites

Corporate Ontologies

CRM

Corporate Structure

16

Selection/Integration/Development

Evaluation Validation

Feedback Tracking

Population

Deployment

Reporting

ENGINEERING

USAGE Corporate Ontology Lifecycle Model (COLM)

Example: Ontology Engineering and Life Cycle

17

Example: Modularization and Integration

Integrated View

Modul 1 …

… Modul n

Modul 2

Modul n-1

Core Ontology

Domain Ontology

Application Ontology

Domain 1 Domain 2

18

Semantic Corporate Search

• Search in non-semantic data

• Search personalization

• Multimedia search

• Search contextualization

Corporate Semantic

Search

19

Example Personalized Search Skill Ontology

Example:

Query „Java“ (+ Personal Skill Profile (Java + C++ Knowledge) )

d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented) = (0.25-0.0.0625) + (0.25-0.0625) = 0.375

sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity)

=> also propose job offers for C++ programmer

20

Semantic Search

Iterative search by the

user.

Advantage: low entry costs

Challenege: query strategy

Text corpus is fact base.

Advantage: unstructured

content accessible

Challenge: ask a valid

question

Background-knowledge

used during search.

Advantage: captures all

latent answers

Challenge: Ontology design

21

Semantic Corporate Collaboration

• Knowledge extraction by mining user activities

• Collaborative tools for modeling ontologies and knowledge

• Dynamic access to distributed knowledge

• Evolution of ontologies and knowledge by collaborative work

Corporate Semantic

Collaboration

22

Information Sources:

Knowledge Management:

Workflows

Knowledge

Semantik

Information

Events & Process Context

Relations &

Interpretation

Content

BPM BPM BPM

BP

M

Workflow Workflow

Literature Colleagues Databases Experts Product Contents

Example: Semantic Collaboration Workflows and BPM

Business Processes

23

Example: Mediated Semantic Business Process Modeling

Heterogeneous

Corporate/Domain

Ontologies

24

Example: Semantic Business Process Management

% receive query and delegate it to another party

rcvMsg(CID,esb, Requester, acl_query-ref, Query) :-

responsibleRole(Agent, Query),

sendMsg(Sub-CID,esb,Agent,acl_query-ref, Query),

rcvMsg(Sub-CID,esb,Agent,acl_inform-ref, Answer),

... (other goals)...

sendMsg(CID,esb,Requester,acl_inform-ref,Answer).

•Paschke, Rule Responder BPM / ITSM Project •Barnickel, Böttcher, Paschke, Semantic Mediation of Information Flow in Cross-Organizational Business Process Modeling, 5th Int. Workshop on Semantic Business Process Management at ESWC 2010 •Adrian Paschke and Kia Teymourian, Rule Based Business Process Execution with BPEL+ , i-Semantics 2009, Graz • Paschke, A., Kozlenkov, A.: A Rule-based Middleware for Business Process Execution, at MKWI'08, München, Germany, 2008.

Rules-enabled BPEL+ Application

BPEL run-time

BRMS (Business Rules

Management System)

events, facts

results

CEP Logic

Reaction Logic

Decision Logic

Constraints

Rule Inference Service

SBPMN -> BPEL+

Prova Rule Engine

Oryx SBPM

25

Corporate Semantic Web

What comes next?

26

Corporate Semantic Web

Corporate Semantic Web (CSW) focuses on the application of

Semantic Web technologies and semantic Knowledge Management

methodologies in corporate environments.

27

Corporate vs. Public Semantic Web

• Closed information systems / Intranet solutions with often known interfaces between systems, services and domains

• Known user groups within enterprise network(s)

• Usage of the existing enterprise IT infrastructure, information, and knowledge is constrained by the existing business rules, policies and workflows/processes

• Data view: closed, often structured data with known data models (e.g., relational, object-oriented, XML, …)

• Logic view: partial closed world assumption, partial unique name assumption, scoped constructive views

28

Social Semantic Web vs. Corporate Semantic Web

• Social Semantic Web = Web of collective knowledge systems

• Focus: Tools in which the central social interactions on the Web plays a role. These tools lead to the development of explicit semantic representations

• Combines technologies, strategies and methods of the Semantic Web, Social Software and Web 2.0

• Finds applications in Corporate Semantic Web as well as Public Semantic Web

29

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

30

Pragmatic Web

Vision: Ubiquitous 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

Mach

ine

Un

ders

tan

din

g

Ubiquitous Next Generation Agents and Smartl Connections

Syntactic Web

Semantic Web

PragamticWeb

HT

ML

XM

L

RD

F

Sm

art

A

ge

nts

Co

nte

nt

Pro

du

ce

r

Passive Active

Co

nsu

me

r

AG Corporate Semantic Web

Freie Universität Berlin

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

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