Intelligent Information and Knowledge Infrastructures Daniel Olmedilla L3S Research Center & Hannover University Intelligent Access to Digital Heritage

IntelligentInformation and

Knowledge Infrastructures

Daniel OlmedillaL3S Research Center & Hannover University

Intelligent Access to Digital Heritage Conference19 Oct. 2007, Tallinn, Estonia

19 Oct. 2007 2Daniel Olmedilla

Outline

L3S Background

Introduction & Motivation

Personalized Search & Ranking

Privacy & Access Control

EU Projects Summary


Outline

L3S Background

Introduction & MotivationIntroduction & Motivation

Personalized Search & RankingPersonalized Search & Ranking

Privacy & Access ControlPrivacy & Access Control

EU Projects SummaryEU Projects Summary


L3S BackgroundMission and Focus

L3S research focuses on innovative and cutting-edge methods and technologies for three key enablers for the European Information Society: Knowledge Information Learning

LS3 projects focus on digital resources and their technological underpinnings:

Digital libraries and Search Semantic Web and Knowledge Sharing Distributed Systems, Networks and Grids

the use of these resources in eLearning and eScience contexts


L3S BackgroundArea “Semantic Web & Digital Libraries”

provide personalized access to distributed information resources and advanced search and recommendation functionalitiesprovide enhanced search on the desktop, in companies, on the Webenhance traditional libraries with digital content and personalized library services


Outline

L3S BackgroundL3S Background

Introduction & Motivation





Introduction & MotivationConference Theme

Intelligent Access to Digital Heritage


Introduction & MotivationUNESCO E-Heritage (I)

Digital Heritage are resources of human knowledge or expression, whether cultural, educational, scientific and administrative, or embracing technical, legal, medical and other kinds of information

Digital materials include texts, databases, still and moving images, audio, graphics, software, and web pages, among a wide and growing range of formats

[ http://portal.unesco.org/ci/en/ev.php-URL_ID=1539&URL_DO=DO_TOPIC&URL_SECTION=201.html, http://portal.unesco.org/ci/en/files/13367/10700115911Charter_en.pdf/Charter_en.pdf ]


Introduction & MotivationUNESCO E-Heritage (II)

Born-digital heritage available on-line, including electronic journals, World Wide Web pages or on-line databases, is now part of the world’s cultural heritage

Using computers and related tools, humans are creating and sharing digital resources - information, creative expression, ideas, and knowledge encoded for computer processing - that they value and want to share with others over time as well as across space


Introduction & MotivationUNESCO E-Heritage (& III)

The purpose of preserving the digital heritage is to ensure that it remains accessible to the public. (…) . At the same time, sensitive and personal information should be protected from any form of intrusion.


Introduction & MotivationFocus of this talk

Intelligent Access to Digital Heritage

SearchRank

• Personalized of media

• Access to sensitiveInformationResources


Introduction & MotivationInformation growth

In today's society, individuals and organisations are, on one hand, confronted with an ever growing load of information and content and, on the other, with increasing demands for knowledge and skills.

To cope with this, we need to link content, knowledge and learning, making content and knowledge more accessible, interactive and usable over time by humans and machines alike.


Introduction & MotivationNot only textual resources


Introduction & MotivationThe 1 TB life (Gordon Bell)

1TB gives you 65+ years of: 100 email messages a day (5KB each) 100 web pages a day (50KB each) 5 scanned pages a day (100KB each) 1 book every 10 days (1 MB each) 10 photos per day (400 KB JPEG each) 8 hours per day of sound - e.g. telephone,

voice annotations, and meeting recordings (8 Kb/s) 1 new music CD every 10 days (45 min each at 128 Kb/s)

It will take you 10 years to fill up your 160 GB drive

Want video? Buy more cheap drives (1 TB/year lets you record 4 hours/day of 1.5 Mb/s video)


Introduction & MotivationMain Objectives

1. Search for textual and audiovisual content

2. Rank results according to relevance

3. Personalize such search and ranking Not all users are the same Find what they are interested in

4. While protecting private information and resources


Outline



Personalized Search & Ranking




Personalized Search & RankingRepresenting context by SW metadata

Metadata for resources can be created by appropriate metadata generators

Ontologies specify context metadata for i.e.:

Emails Files Web pages Publications

Metadata have to be application-independent! Store Metadata as RDF


Personalized Search & RankingPersonalization in the SW

gather online information, integrate heterogenous sources, syndicate according to user’s preferences

embed resources with a personalized context enable users to choose which kind of personalized

guidance in what combination they appreciate as support (plug & learn)

Realization: semi-automated extraction of information from

heterogenous sources re-usable personalization algorithms reason about

distributed data sources (user data, course descriptions, ontologies, etc.)

personalization rules reason about resources, e.g. to make recommendations[Baumgartner, Henze, Herzog. The Personal Publication Reader: Illustrating Web

Data Extraction, Personalization and Reasoning for the Semantic Web. ESWC’05 ]


Personalized Search & Ranking User Knowledge and Interests

Competence: “an effective performance within a domain / context at different levels of proficiency”

Can be explicitly defined by the user or inferred automatically

Competence

ProficiencyLevel

Context

Competency


Personalized Search & Ranking Expanding User Queries with Local Context

User related documents(desktop documents) containing the query

Score and extract keywords

Top query-dependent,

user-biasedkeywords

Extract query expansion or

re-ranking terms

[ Chirita, Firan, Nejdl. Summarizing local context to personalize global web search. CIKM 2006 ]


Personalized Search & Ranking Data heterogeneity

Characteristics A lot of text (unstructured information) A lot of structures, e.g. title, author, creation-date, … Heterogeneity in structure

Different holders (applications) use different schemas In nature, the structure of a domain is too complex for us to

give it a clear and certain definition

Classical Data Integration Transform data into a clear and uniform structure before we use it Intensive human intervention – very laborious and not scalable

Malleable Schema (X. Dong & A. Halevy ’05) Allow overlapping and vague elements to be defined in a single

schema


Personalized Search & Ranking Malleable Schemas: Example Data

Person

DocPerson

email Doc

xml search Jack

Pan

John Gary

True

Xml is the standardfor data exchange

…….

My paper

Dear Sergey, Pleasefind attached the file

…….

25.03.2006Desktop Search

We have many data…….

False

author

author

first name

sur name

name

title

body

Isa book

Isa paper

contents

writersender

attachment

subject

body date


Personalized Search & Ranking Querying Malleable Schemas

For example, user issue query:Q1: Select Person Where first_name Contains “Philip”

To obtain the complete results, we should relax the query to:Q2: Select Person Where first_name Contains “Philip”

Or name Contains “Philip”

A query has to be relaxed to related schema elements

But, how to discover the correlation between schema elements?

Person

…

…

first name

sur namePerson … …

name


Personalized Search & Ranking Discover Schema Correlations (I)

Solution: find duplicates which use different attributes.

Observation:1. more duplicates – better schema correlation discovery2. more accurate schema correlations – better duplicate detection

Solution: Let schema correlation discovery and duplicate detection reinforce each other to achieve improved results


Personalized Search & Ranking Discover Schema Correlations (& II)

title subject author writer Pub-date Rec-date

E1 XML Daniel Jan 1999

E2 XML Daniel Dec 2003

E3 DB Ullman Jul 1994

E4 DB Ullman Nov 2001

E5 AI Stuart Nov 2001

E6 Logic Stuart Nov 2001

duplicates: {E1, E2}, {E3, E4}, {E5, E6}attribute matches: {title, subject}, {author, writer}, {pub-date, rec-date}

duplicates: {E1, E2}, {E3, E4}, {E5, E6}attribute matches: {title, subject}, {author, writer}, {pub-date, rec-date}

[ Xuan Zhou, Julien Gaugaz, Wolf-Tilo Balke, Wolfgang Nejdl. Query Relaxation Using Malleable Schema. SIGMOD’07 ]


Outline




Privacy & Access Control



Privacy & Access ControlAccess Control in Open Systems (I)


Privacy & Access ControlAccess Control in Open Systems (& II)

Assumption: I already know you you have a local account!

Not a member?


Privacy & Access ControlPolicy Examples

Give customers younger than 26 a 20% discount

Up to 15% of network bandwidth can be reserved by paying 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

[ Bonatti, Olmedilla. Driving and Monitoring Provisional Trust Negotiation with Metapolicies. IEEE Policies for Distributed Systems and Networks, 2005 ]


Privacy & Access ControlUse Credentials


Privacy & Access ControlNegotiations

Step 1: Alice requests a service from Amazon

Step 5: Alice discloses her VISA card credential

Step 4: Amazon discloses its BBB credential

Step 6: Amazon grants access to the serviceService

BobAlice

Step 2: Amazon discloses its policy for the service

Step 3: Alice discloses her policy for VISA

[Winsborough, Seamons, Jones. Automated trust negotiation. DARPA Information Survivability Conference and Exposition, 2000 ]


Privacy & Access ControlUser awareness and Control

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

common user

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


Privacy & Access ControlCooperativeness & Verbalization

Suppose Alice's request is rejected

She may want to ask questions like: Why didn't you accept my credit card?

Other possible queries How-to queries What-if queries

Would I get the special discount on financial products X if I were locally employed?

[ Bonatti, Olmedilla, Peer. Advanced policy explanations on the web. ECAI 2006 ]


Privacy & Access ControlSample Screenshot (I)


Privacy & Access ControlSample Screenshot (& II)


Outline





EU Projects Summary


EU Projects SummaryEU IP Nepomuk: Social Semantic Desktop

- Desktop: Help individuals in managing information on their PC

- Semantic: Make content available to automated processing - Social: Enable exchange across individual boundaries

colleague

friend

acquaintance

NEPOMUK enabledpeers

Personal Semantic Web: a semantically enlarged intimate supplement to memory

Social protocolsand distributed search

Email

Person

Topic

WebSite Document

Image

Event

Person


EU Projects SummaryEU IP PHAROS

PHAROS will move forward audiovisual searching from a point-solution search engine paradigm to an integrated search platform paradigm.

PHAROS will integrate future user and search requirements in a living laboratories for innovation

PHAROS partners are from 9 European Countries and will integrate its development with their nationally funded projects. SMEs, academia and large industrial players will ensure maximum impact on the business scenario

PHAROS will use an open approach in integrating external experiences and contributions and exchange results through the PHAROS Federation.

PHAROS will use an specifically-designed management structure, integrating the different PHAROS “streams”

Vision

Integrat

ion

Openess &

Federation

High - Impact


EU Projects SummaryEU NoE REWERSE

REasoning on the WEb with Rules and SEmantics

Web reasoning languages & processing Define set of reasoning languages

Coherent Inter-operable Functionality and application independent

For Advanced Web systems and applications

Advanced Applications as testbeds for languages Context-adaptive Web systems Web-based decision support systems


EU Projects SummaryEU IP TENCompetence


EU Projects SummaryL3S Project Leaders (http://www.L3S.de)

NEPOMUK (http://nepomuk.semanticdesktop.org/Dr. Claudia Niederee

PHAROS - http://www.pharos-audiovisual-search.eu/Dr. Bhaskar Mehta

REWERSE - http://rewerse.net/Prof. Dr. Nicola Henze

TENCompetence - http://www.tencompentece.org/Dr. Daniel Olmedilla


Thanks !

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

Documents

Intelligent Information and Knowledge Infrastructures Daniel Olmedilla L3S Research Center & Hannover University Intelligent Access to Digital Heritage