Introduction to Semantic Web Rules & Policies Daniel Olmedilla, Philipp Kärger L3s Research Center / Hannover University TENCompetence Winter School Innsbruck,

Introduction to Semantic Web Rules &

PoliciesDaniel Olmedilla, Philipp Kärger

L3s Research Center / Hannover University

TENCompetence Winter SchoolInnsbruck, 21st February 2008

Daniel Olmedilla Feb. 21st, 2008TENCompetence WS 2

About this lectureWhy this lecture?

• Lot of noise about the Semantic Web Lot of relevant papers and work on Semantic Web in last

years

• Techniques and tools can be used in the context of lifelong learning and competence development

• Intelligent systems/agents need to be guided

• Software agents Development is expensive Are static Are unflexible


About this lecture Objectives

This lecture is intended to provide reasons that motivated Semantic Web Research

(revisited) a basic understanding of rule-based representation a basic introduction to reasoning techniques a basic understanding of requirements of current

distributed systems a motivation for the use of policies a basic introduction to rule-based policies and their

applications a basic introduction to reactive policies


About this lectureDisclaimer

The objective is to present the main ideas

not an explanation of the theory that lays behind


About this lectureInteractive

And also important This is not

a conference presentation a monologue

Each module partially builds on concepts from previous modules

We provide exercises to strength understanding

You are also encouraged to interrupt and

ASK Questionswhenever you need it


About this lectureThe slides

Slides are wordy so they can be easily understood offline after the tutorial

More definitions and references are available in notes and hidden slides

Tutorial is available from:

http://www.L3S.de/~olmedilla/events/2008/TENC-WS-SWP/20080221_TENC_WS.ppt


OutlineLecture Overview

Whythe Semantic Web?

Introduction

Last Year Lecture

Rule-Based Representation & Reasoning

Semantic Web Policies

Reactive Policies

Conclusions/Summary


OutlineIntroduction


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


IntroductionWarming Up: Problem

Institutions, companies and people need to control the way they Make business Take decisions Offer their assets Etc …

Computers help us on our daily work performing tasks that we cannot perform (or we do it worse) automatically on our behalf

But generally, we need to control how decisions and actions are taken


IntroductionWhat is a policy?

In a very broad way, a policy is defined as

a statement defining the behaviour of an entity


IntroductionPolicies are everywhere

B2B contracts e.g. quantity flexible contracts, late delivery penalties,

etc. Negotiation

e.g. rules associated with auction mechanisms Security

e.g. access control policies Privacy

Information Collection Policies (aka “ P3P Privacy Policies”)

Obfuscation Policies Workflow management

What to do under different sets of conditions Context aware computing

What service to invoke to access a particular contextual attribute

Context-sensitive preferences[ by Norman Sadeh, Semantic Web Policy Workshop panel, ISWC 2005 ]


Exercise 1Specify your own policies

How do you decide (in general terms) which transportation you use to come to this event?

whether you share your

PhD thesis draft?

Pictures from your holidays in Hawaii?

Your famous report so many companies are willing to pay for?

whether you take a private call when being at work?

which tasks you perform everyday at work?


Exercise 1Problem (I)

Now imagine a system application or software agent could/should decide on your behalf. How do you tell such an agent how it should do it?

The way we make business, take decisions, etc. Is dynamic, that is, often changesEvolves with the time

We cannot re-code, re-compile, re-install a new software agent every time we change the way we take decisions


Exercise 1Problem (II)

Furthermore, we need that the system acting on our behalfdoes what we want

How do we tell it? What if we make a mistake and tell

something wrong? is contextual, that is, depends on many factors is “intelligent” (does things as we would do

them) is not reserved only to millionaires


IntroductionThe goal

Build applications/agents whereBehaviour is flexible

Can be changed/updated without re-coding, re-compiling, re-

installing, etc… In a costless manner

Can be managed by administrators/users without needing to be computer experts

Can be understood by normal users


OutlineWhy the Semantic Web?


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


Why the Semantic Web? HTML: in your browser


Why the Semantic Web? HTML: Markup

<h2> Topics </h2><p>Educational Principles <br/>Knowledge Management <br/>Education Process Modeling <br/>Learning Design <br/>Competence Development <br/>…</p><h2> Lecturers </h2><p>Albert Angehrn, INSEAD, France <br/>Boyan Bontchev, Sofia University, Bulgaria <br/>Alexandar Dimov, Sofia University, Bulgaria <br/>Dai Griffiths, University of Bolton, United Kingdom <br/>…</p>

Markup forpresentation only


Why the Semantic Web? HTML: Limitations

HTML deals only with formatting of data

It does not provide information about the data it contains

Query engines do a great job but queries like Give me the list of subjects that the winter school will

deal with Return the affiliations of the lecturers in the winter school

are not possible on the current Web

Search on current Web is based on syntactic matching


Why the Semantic Web? Current Web

• Downloadable Resources: identified by URL's untyped

• Links: href, src, ... limited, non-descriptive

• User: Exciting world

semantics of the resource, however, gleaned from content

• Machine processable: Very little information available

significance of the links only evident from the context around the anchor. [Eric Miller. Weaving Meaning : An Overview of The Semantic Web. 2003 ]


Why the Semantic Web? Semantic Web Definition

“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 LassilaThe Semantic Web, Scientific American, May 17, 2001


Why the Semantic Web? The Semantic Web

• Resources (any resource): Globally Identified by URI's Extensible Relational

• Links: Identified by URI's Extensible Relational

• User: Even more exciting world,

richer user experience • Machine:

More processable information is available (Data Web)

• Computers and people: Work, learn and exchange

knowledge effectively

[Eric Miller. Weaving Meaning : An Overview of The Semantic Web. 2003 ]


OutlineLast Year Lecture


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


Last year lectureWarning (or clarification )

OWL: Web Ontology Language

Ontology = OWL


Last year lectureIntroduction to Semantic Web

Resource Descriptions & Vocabularies (I)

Queryingthe SW (I)

Reasoningon the SW (I)


Resource Descriptions & Vocabularies (II)

Queryingthe SW (II)

Reasoningon the SW (II)

Summary

Basic Concepts

AdvancedConcepts

Introduction


Last year lectureThe Semantic Web Stack

XML / Namespaces

URI / Unicode

Last year lecture

Part of this year lecture


OutlineRule-Based Representation & Reasoning


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


Rule-Based Representation and ReasoningWho uses logic?

Aristoteles

Spock

Mathematicians

Computer scientists

You


Exercise 1Revisited (I)

Were your policies declarative?

That is, they specify the what (conditions) but not the how (algorithm or process to satisfy them)

E.g., HTML pages describe what the page should contain but not how to actually display the page on a computer screen

using inference rules?

E.g., If destination is in Europe then max price is …

E.g., If distance is less than … then go by train

if not, do you think they are more naturally modelled as rules?


Rule-Based Representation and ReasoningRules are everywhere (I)

Rules of ethics for robots

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2. A robot must obey orders given to it by human beings, except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

[Isaac Asimov. Runaround. 1942 ]


Rule-Based Representation and ReasoningRules are everywhere (II)

Declarative


Rule-Based Representation and ReasoningRules are everywhere (III)


Rule-Based Representation and ReasoningInference Rule (I)

Relation holding between premises (antecedent) and conclusions (consequent)

The conclusion is said to be inferable (or derivable or deducible) from the premises

We can infer new knowledge


Rule-Based Representation and ReasoningInference Rule (II)

Rule notation: consequent ← antecedent

Stands for antecedent consequentthat is, IF antecedent THEN consequent

Examples: If someone is a man then he is mortal

mortal(X) ← man(X). If someone is in this lecture, then he/she is a researcher

researcher(X) ← inThisLecture(X).It does not matter what X is, the rule is always valid.

Base for deductive reasoning


Rule-Based Representation and ReasoningDeductive vs. Inductive Reasoning

Deductive: proceeds from general principles or premises to derive particular information (conclusions).

Example All apples are fruit. All fruits grow on trees. Therefore all apples grow on trees.Remember Sherlock Holmes?

Inductive: the premises of an argument are believed to support the conclusion but do not ensure its truth.

Makes generalizations (from empirical observations)

Example All observed crows are black. Therefore all crows are black.


Rule-Based Representation and ReasoningExample: information about your family

Assume an agent needs to know all the information about your closest relatives.

How do you inform your agent about such information?


Rule-Based Representation and ReasoningPossibility 1: Enumerate all the facts

Try to enumerate all that information for your agent:

Tom is the father of MaryTom is the parent of MaryAlice is the sister of MaryMary is the sister of AliceClara is the sister of MaryMary is the sister of ClaraMary is the mother of AnneMary is the parent of Anne

Tom is the grandparent of AnneAlice is the aunt of AnneClara is the aunt of AnneClara is the mother of BobAlice is the aunt of BobMary is the aunt of BobTom is the grandparent of Bob…


Rule-Based Representation and ReasoningPossibility 2: facts + rules + deduction

Tom is the father of Mary father(‘Tom’,’Mary’). Alice is the sister of Mary sister(‘Alice’,’Mary’). Clara is the sister of Mary sister(‘Clara’,’Mary’). Mary is the mother of Anne mother(‘Mary’,‘Anne’). Clara is the mother of Bob mother(‘Clara’,‘Bob’).

A parent is either a father or a motherparent(P,C) ← father(P,C) mother(P,C).

The parent of your sister is your parentparent(P,C) ← parent(P,X) sister(X,C) .

The parent of a parent is a grandparentgrandparent(P,C) ← parent(P,X)

parent(X,C). An aunt is the sister of a parent

aunt(A,C) ← sister(A,X) parent(X,C) .

Axioms/Facts

Inference Rules


Rule-Based Representation and ReasoningExercise 2: deductive reasoning

Given such a program, write down the inferred new knowledge

Tom is the father of Mary father(‘Tom’,’Mary’). Alice is the sister of Mary sister(‘Alice’,’Mary’). Clara is the sister of Mary sister(‘Clara’,’Mary’). Mary is the mother of Anne mother(‘Mary’,‘Anne’). Clara is the mother of Bob mother(‘Clara’,‘Bob’).

A parent is either a father or a motherparent(P,C) ← father(P,C) mother(P,C).

The parent of your sister is your parentparent(P,C) ← parent(P,X) sister(X,C) .

The parent of a parent is a grandparentgrandparent(P,C) ← parent(P,X)

parent(X,C). An aunt is the sister of a parent

aunt(A,C) ← sister(A,X) parent(X,C) .


Rule-Based Representation and ReasoningExercise 2: solution

Given such a program, write down the inferred new knowledge

From first rule: Tom is the parent of Mary parent(‘Tom’,’Mary’). Mary is the parent of Anne parent(‘Mary’,’Anne’). Clara is the parent of Bob parent(‘Clara’,’Bob’).

From second rule (+ the first rule): Tom is the parent of Alice parent(‘Tom’,’Alice’). Tom is the parent of Clara parent(‘Tom’,’Clara’).

From the third rule (+ the first and second) Tom is the grandparent of Anne

grandparent(‘Tom’,’Anne’). Tom is the grandparent of Bob

grandparent(‘Tom’,’Bob’).

From the forth rule (+ the first rule) Alice is the aunt of Anne aunt(‘Alice’,’Anne’). Clara is the aunt of Anne aunt(‘Clara’,’Anne’). Mary is the aunt of Bob aunt(‘Mary’,’Bob’). Alice is the aunt of Bob aunt(‘Alice’,’Bob’).


Rule-Based Representation and ReasoningAdvantages

Declarative Infer implicit knowledgeCompact representationWell-defined semanticsAvailable proofsTruths that it establishes are absolute


Rule-Based Representation and ReasoningDisadvantages

Wrongly specified rules wrong implicit knowledge It must have some truths in hand before starting

Sometimes you don’t have them all Sometimes not all is true or false You need to specify all right rules

Otherwise, underspecified programs


OutlineSemantic Web Policies


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


Semantic Web PoliciesWhat is a policy? Definitions

A statement defining the behaviour of an entity

An enforceable, well-specified constraint on the performance of a machine-executable action by a subject in a given situation

A deliberate plan of action to guide decisions and achieve rational outcome(s).


Semantic Web PoliciesA broader notion of policy

The term policy covers: Security/Privacy policies, Trust management

Business rules

Quality of Service directives

Service-level agreements

Communication and conversation policies and more...


Semantic Web PoliciesAn e-learning scenario (I)

Exploiting agents to support collaborative learning in an on-line learning community:

They offer means to handle this complex setting as we will learn from the following four scenarios


Semantic Web PoliciesAn e-learning scenario (II)

“Only my tutor is able to access myhomework. My fellow students are able to access my lecture notes but not my homework.”

Access control

Security

Trust management


Semantic Web PoliciesAn e-learning scenario (III)

“I want to be reminded two days before my homework is due.”

“I want to get an SMS if my tutor extends a homework’s deadline.”

Reactive Agents• Events (e.g., deadline

extension) trigger agent decisions


Semantic Web PoliciesAn e-learning scenario (IV)

“While using my e-learning tool I only want to receive chat messages from my fellow students and my tutor. Others get an automatic reply ‘Please contact me later, I am busy’.”

Communication Control


Semantic Web PoliciesAn e-learning scenario (V)

“In order to purchase learning material I use my Credit Card only with parties providing the ‘Online Security Certificate’.”

Agent NegotiationsPrivacy

Step 4

Step 1

Step 3

Step 2


Semantic Web PoliciesAn e-learning scenario: Using policies

• the whole system becomes more flexible• for different behavior change the policy (not the whole

software)• communication in the community gets more

personalized• “My fellow students should not disturb

me when I am at work.”• automatically generated explanations

• “You cannot send me a chat message because …”

• “Your tutoring agent alerts because …”• “You cannot access your fellow’s

homework because …”• policies are reactive

• “As soon as I idle for two days, send me …”• “If a deadline is extended then …”


Semantic Web PoliciesNaturally expressed as rules

If customers are younger than 26 give a 20% discount on international tickets

Up to 15% of network bandwidth can reserved if payment is done with an accepted credit card

Customers can rent a car if they are 18 or older, and exhibit a driving license and a valid credit card


Semantic Web PoliciesBenefits

Explicit license for autonomous behaviourReusabilityEfficiencyExtensibilityContext-sensitivityVerifiabilitySupport for simple as well as sophisticated

agentsProtection from poorly-designed, buggy or

malicious agentsReasoning about agent behaviour


Semantic Web PoliciesRequirements

Many policies, one framework Integration with external sourcesPolicies as active objects

Executing actionsNegotiationsUser awareness and controlCooperative enforcement


Semantic Web PoliciesMany policies, one framework

It is appealing to integrate all policies in one framework

One common infrastructure for interoperability and decision

making

Where policies can be harmonized & coordinated


Exercise 1Revisited (II)

Were your policies requiring extra knowledge

Who are your colleagues and your boss

Who works in your project

What a valid credit card is

Distance between XYZ and Innsbruck is …, Innsbruck is in Austria, my institution does not allow me to take a plane if …, allowed max price for a flight to Innsbruck would be …

referencing to properties of requesters?

Sources of this information?

All in our knowledge base?


Semantic Web PoliciesIntegration with external systems

Policies are not islands

Decisions need data, information, and knowledge

Each organization has its own

Already available through legacy software and data

A realistic solution must interoperate with them

Third parties

Credit card sites for validity checking

External databases Variety of web resources


Semantic Web PoliciesPolicies are not only passive objects

Policies may specify Exchange of signed information (e.g., digital credentials) Event logging

Failed transactions must be logged Log downloads of new articles for one week

Communications and notifications Notify the administrator about repeated login failures

Workflow triggering such as (partly) manual registration procedures

i.e. Policies may specify actions To be interleaved with the decision process


Semantic Web PoliciesNegotiations

Step 1: Alice requests a service from Bob

Step 5: Alice discloses her VISA card credential

Step 4: Bob discloses his BBB credential

Step 6: Bob grants access to the serviceService

BobAlice

Step 2: Bob discloses his policy for the service

Step 3: Alice discloses her policy for VISA


Exercise 1Revisited (III)

Suppose

Your policy is given to you by your employer

You have to explain your policy

You submit a paper and you get “Rejected”


Semantic Web PoliciesUser awareness and control

Explain policies and system decisions Make rules & reasoning intelligible to the common

user

Encourage people to personalize their policies Make it easy for users to write their own rules

Use natural language?

“Academic users can download the files in folder historical_data whenever their creation date precedes 1942”

Suitably restricted to avoid ambiguities

Fortunately, users spontaneously formulate rules


Semantic Web PoliciesCooperative Policy Enforcement

Crucial for the success of a service

Never say (only) “no”!

Encourage first-time users

Who don't know how to use your service

Explain policy decisions

Especially failures

Advanced queries: Why not

Advanced queries: How-to, What-if

You can’t open this door, but

you can ask Alice for permission


Semantic Web PoliciesSome solutions already available

Features available out of the box:Expressive policy languages and frameworks Integrated relational databases, RDF stores,

file systems requests, time and location-aware packages, etc.

Execution of actions such as logging facilities, exchange of credentials, etc.

Policy driven negotiations and preferencesAutomatically generated explanations

Demo at http://policy.L3S.uni-hannover.de/


OutlineReactive Policies


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


Reactive PoliciesEvent-Condition-Action (ECA) Policies

provide a more flexible notion of policies so far, policies were not able to react,

i.e., to handle events so far, actions where only included as internal or

provisional actions not as a re-action usually of the form

ON eventIF conditionDO action

ON receiving new call IF user not availableDO automatic reply


Reactive PoliciesEvents

trigger the execution of a rule can be simple events:

e.g., “ON receiving new call” or more complex

ON receiving new calland at the same time another call comes inand there were no calls in the last 10 minutes

to define complex events we need an event algebra


Reactive PoliciesEvent algebra

Assume events happen at a certain point of time The algebra allows us to combine events to create more

complex ones Example operators:

Both E1 and E2 happens at the same time E1 happened before E2 m events out of n happened in an arbitrary order E1 and E2 occurred and E2 did not occur …

the complexity of the event algebra used depends on the purpose of the ECA-based system

events have to be stored in a history in order to check against complex combination of events

algorithms for the detection and tracing of complex events are non trivial


Reactive PoliciesConditions

• handled like the policies we had so far:• they may include external actions to prove the

conditionsuch as a database or web service querye.g., “… if the there is snow in Innsbruck …

”• they may include negotiations to prove the

condition such as “…if Credit Card is valid …”


Reactive PoliciesActions

• could be single actions• could also be combinations of actions

Sequential execution Do Action1 and then Action2 and then

Action3 Parallel execution

Do Action1 and Action2 at the same time More complex combinations possible


Reactive PoliciesDo you use them?

Do you think this is nothing you need to know about?

Do you think you have never used this?

Do you think this is too complicated for any user to use?

Does this sound familiar to you?

ON new e-mail arrivalIF subject contains “[SPAM]”DO move e-mail to folder “filtered_spam”


Reactive PoliciesMany other applications

• DB triggers: incremental maintenance of data (databases, XML, RDF, etc.)

• cleansing of input data streams• automatic repairs in case of constraint violation• broadcasting of changes in documents to subscribers• maintaining statistics about website usage• Active databases (update correlated fields in case

others are updated)• network management• business processes (specification and

implementation)• And many more


Reactive PoliciesA communication example

Problem: The behavior of a messenger is not well adjustable:

Most of you probably know this:


Reactive PoliciesProblem

• arbitrary people bother you with chat messages• they may even call you• for some of them you want to offer an answering

machine• some you just want to block • people send you files – how could you trust them?• the messenger allows other calls while you are

currently answering a call• although your messenger stores the

birthdays of your friends you forget about them because it does not remind you.


Reactive PoliciesA possible solution with ECA-policies (I)

Why not use ECA-policies to let your agent solve the problem for you?

ON new receiving callIF caller is a friend of mine

ANDthere is no other currently ongoing call

DO accept call AND put it on the speakers

ON new receiving fileIF sender is a friend of mine

ORsender provides a certificate AND certificate is valid

DO accept file AND store it on folder “received_files”


Reactive PoliciesA possible solution with ECA-policies (II)

Even an automatic birthday reminder

ON new day (timer raised once per day)

IF there is a person in the winter school list

AND

it is his/her birthday today

DO send a chat message with text “Happy Birthday”


Reactive PoliciesExercise 3: ECA Policies

See given exercise sheet


Reactive PoliciesExercise 3: Solution

Actions executed

Pop up window with a reminder about the exam registration

First call to my skype client Second call to my wife’s phone


OutlineConclusions/Summary


Introduction

Last Year Lecture



Reactive Policies

Conclusions/Summary


ConclusionsSummary (I)

Hopefully this tutorial helped you to get a brief idea about

reasons that motivated Semantic Web Research a basic understanding of rule-based representation a basic introduction to reasoning techniques a basic understanding of requirements of current

distributed systems a motivation for the use of policies a basic introduction to rule-based policies and their

applications a basic introduction to reactive policies


ConclusionsSummary (II)

Everyday systems/agents take over new tasks we would otherwise perform ourselves

They can do some/many of them faster and better than us

But they are not “intelligent” as we are

We need to tell them what to do/how to behaveRule-based Policies + reasoning help you to do that Dynamically and allowing evolution Flexibly With well defined semantics and interoperability At low cost


ConclusionsSummary (III)

But

That brings in many new issues like Required expressiveness for an application

scenario Usability problems User Awareness Verification/validation of policies …


ConclusionsFinal message


References

• RDF Primerhttp://www.w3.org/TR/rdf-primer/

• Antoniou et al., Rule-based policy specification. Secure Data Management in Decentralized Systems. Springer, 2007.http://www.l3s.de/~olmedilla/pub/2007/2007_bookDDMS_rule_policies.pdf

• Bonatti, Olmedilla. Rule-based policy representation and reasoning for the semantic web. In Reasoning Web, Third International Summer School 2007. Springer.http://www.l3s.de/~olmedilla/pub/2007/2007_ReasoningWeb-policies.pdf

• Bradshaw et al., Making Agents Acceptable to people, Intelligent technologies for information analysis: Advances in agents, data mining and statistical learning. Springerhttp://www.ihmc.us/research/projects/KAoS/biit-jeff.pdf

• De Coi et al., Exploiting policies in an open infrastructure for lifelong learning. In EC-TEL, Crete, Greece, Sep 2007. Springer. http://www.l3s.de/~olmedilla/pub/2007/2007_ec-tel_policies.pdf


Questions?

[email protected] – http://www.L3S.de/~olmedilla/

Thanks!

Documents

Introduction to Semantic Web Rules & Policies Daniel Olmedilla, Philipp Kärger L3s Research Center / Hannover University TENCompetence Winter School Innsbruck,