Presentation by Adrian Paschke about Corporate Semantic Web – The Semantic Meets the Enterprise, at SemTechBiz Berlin 2012, Feb, 6-7, 2012

1. 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. 2
Agenda
• About Corporate Semantic Web
• Corporate Semantic Engineer
• Corporate Semantic Search
• Corporate Semantic Collaboration
• Summary and Future

3. 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. 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. 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. 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
CSCWDBMSBPM
ITSM
ERP
SRM

7. 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. 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. 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. 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. 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. 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. 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. 14
• Ontology modularization and
integration
• Ontology versioning
• Ontology cost estimation models for
corporation
• Ontology evaluation
Corporate Semantic Engineering
Corporate
Semantic
Engineering

15. 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. 16
Selection/Integration/Development
EvaluationValidation
Feedback
Tracking
Population
Deployme
nt
Reporting
ENGINEERING
USAGE Corporate
Ontology
Lifecycle
Model
(COLM)
Example: Ontology Engineering and Life Cycle

17. 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. 18
Semantic Corporate Search
• Search in non-semantic data
• Search personalization
• Multimedia search
• Search contextualization
Corporate
Semantic
Search

19. 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. 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. 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. 22
 Information
Sources:
Knowledge
Management:
Workflows
Knowledge
Semantik
Information
 Events & Process
Context
Relations
&
Interpretation
 Content
BPM BPMBPM
BPM
Work
flow Workflow
Literature Colleagues Databases Experts
Product Contents
Example: Semantic Collaboration Workflows and BPM
Business
Processes

23. 23
Example: Mediated Semantic Business Process Modeling
Heterogeneous
Corporate/Domain
Ontologies

24. 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. 25
Corporate Semantic Web
What comes next?

26. 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. 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. 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. 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. 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
Machine
Understanding
Ubiquitous Next Generation Agents and Smartl Connections
Syntactic
Web
Semantic
Web
Pragamtic
Web
HTML
XML
RDF
Smart
Agents
Content
Producer
Passive Active
Consumer

31. AG Corporate Semantic Web
Freie Universität Berlin
http://www.inf.fu-berlin.de/groups/ag-csw/
http://www.corporate-semantic-web.de