semantic methods for electronic markets rudi studer 1,2,3, anupriya ankolekar 1,2, nenad stojanovic...

Semantic Methods for Electronic Markets

Rudi Studer1,2,3, Anupriya Ankolekar1,2, Nenad

Stojanovic2

Institute AIFB, University of Karlsruhewww.aifb.uni-karlsruhe.de/WBS

FZI Research Center for Information Technologies

www.fzi.de

Ontoprise GmbHwww.ontoprise.de

SRI, Menlo ParkAugust 10, 2006

http://www.ontoprise.de/




August 10th, 2006, SRI 2

Outline

• Semantic Technologies and Services @ Karlsruhe

• Services Vision and its Challenges• Semantic Services

– Service Matching: Discovery and Selection– Semantic Business Process Management– Semantic Compliance Management

• Conclusion


Karlsruhe: Location for Semantic Technologies

Knowledge Management

Community Support

Electronic Markets

eGovernment

Semantic Web Infrastructure

Ontology Management

Data and Text Mining

Peer-to-Peer

Semantic Web Services

Application-orientedResearch

Know-how TransferRealizing new Scenarios

Application-orientedResearch

Product DevelopmentInnovative Solutions

Basic ResearchApplication-oriented

Research

AIFB


Who are we? … Semantic Technologies Research Group

FZIAIFB

Rudi Studer

Valentin Zacharias

Max Völkel

Nenad Stojanovic

York Sure

Andreas Abecker

Ljiljana Stojanovic

Johanna Völker

Stephan Bloehdorn

Sudhir Agarwal

Jens Hartmann

Philipp Cimiano

Mark Hefke

Stephan GrimmPeter Haase

Steffen Lamparter

Saartje Brockmans

Pascal Hitzler

Denny Vrandecic

Christoph Tempich

Markus Krötzsch

Anupriya Ankolekar

Hans-Jörg Happel

Heiko Haller

Holger Lewen

Sebastian Blohm

Yimin Wang

Julien Tane

Knowledge Management

Community Support

Electronic Markets

eGovernment

Ontology Management

Data/Text Mining

Peer-to-Peer

Semantic Web Services

Simone Braun

& ~40 people at Ontoprise

Sebastian Rudolph

Duc Thanh Tran

Tuvshintur Tserendorj


Semantic Web Services Projects

SmartWeb Mediating mobile, intelligent access to Web services, such as weather, route information etc.

DIP Data, Information, and Process Integration with Semantic Web Services Reasoning infrastructure for Semantic Web services

SESAM Semantic matching of energy products and legal contracts in P2P electricity markets

IME Graduate school of information management and market engineering

– Ontology-based policies for buyer preferences and seller pricing– Mapping between different ontology and rule formalisms and their visual

modelling via meta-modeling

Billing the Grid Accounting and pricing resource usage in Grid environments based on negotiation and policies

FIT Fostering self-adaptive e-government service Improvement using semantic Technologies SAKE Semantic-enabled Agile Knowledge-based e-government

SAP Cooperation projects in the context of SOA


Outline




• Conclusion


The vision of a service-oriented world

• Automated B2B commerce• Electronic trading

marketplaces • Business process outsourcing

and integration on the Web• Resource sharing, distributed

computation• Company eco-systems


Challenges

1. How to discover the right services?– For my business needs and objectives– UDDI common solution, but insufficient: need

programmer agreement, search through keywords

2. How to compose services (automatically)?– Create plan or template with unknown and/or

unreliable services (planning operators)– Service composition needs to go beyond functional

composition for real business needs

3. How to select the services to invoke or serve?– Multi-attributive services with differing QoS


Challenges

4. How can services talk to each other?– Interoperability of data, protocols, processes,

applications– Common standards help, but manual agreement

still required

5. How to establish trust among the participants of a transaction?– legal responsibilities of participants


Our approach: Semantic Services

Semantic annotations and reasoning to address several challenges:

1. How to discover the right services?– semantics for richer descriptions of services for

automated, more accurate service discovery

2. How to compose services (automatically)?– semantic service descriptions for automated

composition, incl. preconditions and effects – semantic descriptions of constraints and goals

3. How to select the services to invoke or serve?– Semantic description of multi-attribute services and

preferences used for selection decision


Our approach: Semantic Services

4. How can services talk to each other?– Semantic interoperability via the use of shared

expressive ontologies for modelling services

5. How to establish trust among the participants of a transaction?– On-the-fly contracting using semantic contract

descriptions– Reputation mechanisms


Outline



– Service Matching: Discovery and Selection

– Semantic Business Process Management– Semantic Compliance Management

• Conclusion


Scenario: Matchmaking

Which service can I use to generate optimal routing as part of my Order-To-Cash business process?

Provider 1

Provider 2

Provider n

Sales order

Delivery

Picking

Shipment

Packing

RoutingService Discovery• Need service that can fit

in existing workflows• Real business needs go

beyond functional matching of services, e.g. for Business Process Outsourcing


Approach

• Model services (offers) and requests declaratively in terms of – Functional and temporal attributes– Service configurations– Preconditions and desired effects/actions of

services

– Business policies, access control policies etc.

• Matchmaking answers include conditions under which a service is a match

[see publications by Agarwal, Ankolekar, Lamparter]

functional

context-driven


Modelling Web Services

• Describe temporal structure of a web service with -calculus– Composition is a central aspect of -calculus

– Typed communication channels can capture relationships between input and output activities

– Describe involved objects (-calculus names) semantically with description logics

– Embed access control policies as -calculus conditions


Scenario: Matchmaking

Which service can I use to generate optimal routing as part of my Order-To-Cash business process? Provider 1

Provider 2

Provider n

Sales order

Delivery

Picking

Shipment

Packing

Routing

Service Selection• How to choose which service to

use?• Select best service from set of

matching services– Preference-based selection for

configurable services– Auctions for globally optimal

service allocation– Take business context into

account


Service Selection

“I need a service with encryption key ≥ 128 bits, response time < 10s andprice < ´5 Euro”

encryption key ≤ 512 bits response time = 5sprice = 3 Euro

Automatic selection and negotiation of configurable Web services requires:

Preference information within the admissible range Cardinal preferences to make multi-attributive decisions

Agent

WS Provider I

encryption key = 128 bits response time = 3sprice = 4 Euro

WS Provider II

What key length should be chosen?

Is a 2 sec. improvement in response time worth 1 Euro of additional cost?


Policies for Service Selection

Scoring Policy:maxPrice(key,rt) = 0.1*key+0.7*rt

Pricing Policy

minPrice(key,rt) = 0.05*key+0.04rtAgent

WS Provider I

encryption key = 128 bits response time = 3sprice = 4 Euro WS Provider II

We distinguish between • Scoring Policies: Rules determine which configurations are

admissible and the willingness to pay for a certain configuration • Pricing Policies: Rules that determine what configurations are

provided and their actual price

Challenge: How can we model scoring and pricing policies using Web standards and use them in the decision making process.


Preference-based Selection

• Matchmaker needs to know– How does the price depend on service properties? (Pricing

Policies)

– How does requester’s willingness to pay depend on service properties? (Scoring Policies)

• Matchmaker ranks, e.g., services according to scoring policy

Encode these policies in requests and offers

Our Approach: Policy Ontology Utility Function Policies (Allows deriving rankings, assess absolute suitability,

conflict resolution)Declarative approach based on a foundational ontology (high degree of

axiomatization)

Internet standards: XML, OWL-DL, SWRL (DL-safe subset)Policy enforcement based on logical reasoning using KAON2 reasoner


Modeling Utility Functions

How to model a utility function with ontologies?

Function

PointpolicyValue : Datatypevaluation : Float

next

PiecewiseLinearFunction

constitutedBy constitutedBy

PatternBasedFunction

patternIdentifier : StringpatternParameter1 : Float.patternParameterN : Float

010s

1

5s policyValue

valuation

PointBasedFunction

yes no

1

policyValue

valuation

0 10s

1

5s

valuation

policyValue

AttributePolicyisEvaluated

WRTdefines

defines


Evaluation


Outline



– Service Matching– Semantic Business Process

Management– Semantic Compliance Management

• Conclusion


Matchmaking using the business context

contextual requirements

Pricing policy:“Pay no more than 1K EURO“ “Sell low quality for low price“

Business goal: “High quality of the process“

Provider 1

Provider 2

Provider n

Sales order

Delivery

Picking

Shipment

Packing

Routing


Big picture: Contextualized Business

Policies

business process

business rules

business strategy

business goals

Vertical information integration

business innovatio

n

business collaboration


SemBPM Prototype

Policies


Outline




• Conclusion


Compliance Management

• „Through 2008, investigation of new technologies will slow as discretionary budgets divert to regulatory compliance.… in many case, discretionary IT budgets are entirely consumed by compliance efforts …“

Source: Gartner's Top Predictions for 2006 and Beyond

• e.g. Sarbanes Oxley, Basel II

• Drawbacks of existing solutions for the automation of ComplMgm– non-flexibility due to hard coding of regulations– non-reusability due to lack of a formal description– possible inconsistencies due to an isolated view on the

regulations A formal approach is needed


Semantic Compliance Management: general approach

Policies

Semantic policy model

Semantic business process model

Target domain

Automatic C

ompliance check


Compliance Management for Internal Controls

• Sarbanes Oxley (SOX) and related Compliance Requirements are an Implementation for Management of Internal Controls forced by Law

• COBIT (Control Objectives for Information and related Technology) serves as conceptual framework

– Example for an Application Control (AC):

„Each Purchase Order (PO) with an amount higher than 5000 Euros must be approved by two different Purchasers (Double Check Control)“

• Main Idea: Express Application Controls (ACs) as SWRL-Statements (DL-Safe-Rules) and guard semantic instances of Business Processes during runtime


RiskAdvice. . .

Guarded Sequence (GS)

bp

Semantic Mirror of

bpCS(bp)

Facts(bp)Inference

3

4

GS(bp)

LogsLogs(bp)

2a

2b

1a. . .

Knowledge Base

1b

GA = Guarded Activity is an activity which is in scope of an Application Control (AC) on a Business Process (bp)GS = Guarded Sequence is a sequence of guarded activitiesCS = Control Statement is a logical expression which defines an AC and the reaction on its violation in the form of

Riskii AdvicebpGSacviolationacbpCS ))(,(:),(

StartGA EndGA

Compliance Management for Internal Controls


Semantic Compliance Management: Example


Outline



– Service Matching– Semantic Business Process Management– Semantic Compliance Management

• Conclusion


Conclusion

Future Work:• Composition of services• Sound theoretical basis for combining

description logics and process algebras• Contract modelling and monitoring• Semantic business process

management– Modeling of strategic knowledge– Semantic monitoring of BPs


Open Questions

• What level of semantic annotation is required– Scalable semantic reasoning– Business value of semantic services– Who will provide the semantic annotations?

• To which extent will business knowledge be formalized– Business rules, strategy, goals

• How to elicit the utility functions?


Main EU Semantic Web Projects in KA

SEKT: Semantically-enabled Knowledge Technologies– €12.5M, 3 year project, 11 European partners (Techn. Coordinator)– http://www.sekt-project.org/

DIP: Data, Information, and Process Integration with Semantic Web Services

– €16.3 M, 3 year project, 17 European partners– http://dip.semanticweb.org/

Knowledge Web– Network of Excellence, €8M, 4 year project, 18 European partners– http://knowledgeweb.semanticweb.org/

X-Media: Knowledge Sharing and Reuse across Media– €13M, 4 year project, 15 European partners– http://nlp.shef.ac.uk/X-Media/index.html

NeOn: Networked Ontologies– €10M, 4 year project, 14 European partners (Techn. Coordinator)– http://www.neon-project.org/

Nepomuk: The Social Semantic Desktop– €11 M, 3 years project, 16 european partners– http://nepomuk.semanticweb.org/

http://www.sekt-project.org/



http://dip.semanticweb.org/



http://knowledgeweb.semanticweb.org/



http://nlp.shef.ac.uk/X-Media/index.html




http://www.neon-project.org/

http://nepomuk.semanticweb.org/




“Shaping the future infrastructures for semantic applications”

The Open University (co-ordinator)University of Sheffield

Universidad Politecnica Madrid,iSOCO, pharmaInnova, Atos Origin

Universitaet Karlsruhe, Software AG, ontoprise, Universitaet Koblenz-Landau

Institut ‘Jozef Stefan’

INRIA Alpes

United Nations FAO, CNR-LOA

Lifecycle Support for Networked Ontologies

• EU IST Integrated Project (FP6)– Start date: March 2006– Duration: 4 year project – Funding: € 10M – http://www.neon-project.org/

• Key outcomes from NeOn– Open, scalable and service-centred reference

architecture– The NeOn toolkit – for engineering contextualized

networked ontologies and semantic applications – Industry-strength documentation and reference material – Three case studies in two sectors:

pharmaceuticals and agriculture/fisheries

http://www.neon-project.org/


Semantic MediaWiki

• MediaWiki used for Wikipedia• Semantic MediaWiki introduces some additional

markup into the wiki-text which allows users to add ”semantic annotations”.

• Structured Knowledge Representation (with RDF export)

• Extensions– for typed Links

• Previously: … Karlsruhe is located in [[Germany]] …• New: … Karlsruhe is located in

[[LocatedIn::Germany]] …– for Annotations

• Previously: … Karlsruhe has 280.000 inhabitants …• New: … Karlsruhe has [[Inhabitants:=280000]] …


More information at http://wiki.ontoworld.org/


References

• Specification of Invocable Semantic Web Resources, Sudhir Agarwal, ICWS 2004

• Automatic Matchmaking of Web Services, Sudhir Agarwal, Anupriya Ankolekar, WWW2006 Poster

• A Policy Framework for Trading Configurable Goods and Services in Open Electronic Markets. Steffen Lamparter, Anupriya Ankolekar, Rudi Studer, Christof Weinhardt, ICEC 2006

• Specification of Access Control and Certification Policies for Semantic Web Services, Sudhir Agarwal, Barbara Sprick, EC-Web 2005

• Towards a Formal Verification of OWL-S Process Models. Anupriya Ankolekar, Massimo Paolucci, Katia Sycara, ISWC 2005

• Automatic Matchmaking of Web Services. Sudhir Agarwal and Rudi Studer. International Conference on Web Services (ICWS 2006)

• Approximating Service Utility from Policies and Value Function Patterns. Steffen Lamparter, Andreas Eberhart, Daniel Oberle, 6th IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY 2005)

• Specification of Policies for Automatic Negotiations of Web Services, Steffen Lamparter, Sudhir Agarwal, Proceedings of the Semantic Web and Policy Workshop at ISWC 2005


Thanks!

Prof. Dr. Rudi [email protected]

www.aifb.uni-karlsruhe.dewww.fzi.de

www.ontoprise.de

semantic methods for electronic markets rudi studer 1,2,3, anupriya ankolekar 1,2, nenad stojanovic...

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