Intelligent Information and Knowledge Infrastructures

1
IntelligentInformation and Knowledge
Infrastructures
Intelligent Access to Digital Heritage
Conference19 Oct. 2007, Tallinn, Estonia

• Daniel Olmedilla
• L3S Research Center Hannover University

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Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

3
Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

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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

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L3S BackgroundArea Semantic Web Digital
Libraries

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

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Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

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Introduction MotivationConference Theme

• Intelligent Access
• to Digital Heritage

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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_ID153
9URL_DODO_TOPICURL_SECTION201.html,
http//portal.unesco.org/ci/en/files/13367/1070011
5911Charter_en.pdf/Charter_en.pdf
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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
  worlds 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

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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.

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Introduction MotivationFocus of this talk

• Intelligent Access
• to Digital Heritage

Search Rank

• Personalized of media
• Access to sensitive

Information Resources
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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.

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Introduction MotivationNot only textual
resources
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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)

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Introduction MotivationMain Objectives

• Search for textual and audiovisual content
• Rank results according to relevance
• Personalize such search and ranking
• Not all users are the same
• Find what they are interested in
• While protecting private information and resources

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Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

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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

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Personalized Search RankingPersonalization in
the SW

• gather online information, integrate heterogenous
  sources, syndicate according to users
  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. ESWC05
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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

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Personalized Search Ranking Expanding User
Queries with Local Context
Extract query expansion orre-ranking terms
Top query-dependent,user-biasedkeywords
Score and extract keywords
User related documents(desktop documents)
containing the query
Chirita, Firan, Nejdl. Summarizing local
context to personalize global web search. CIKM
2006
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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

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Personalized Search Ranking Malleable Schemas
Example Data
xml search
Jack
first name
Person
Xml is the standardfor data exchange.
sur name
title
Pan
body
author
Doc
name
Person
author
John Gary
Isa book
False
writer
sender
Isa paper
subject
email
True
My paper
Doc
attachment
contents
date
body
Dear Sergey, Pleasefind attached the file.
Desktop SearchWe have many data.
25.03.2006
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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 toQ2 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?

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Personalized Search Ranking Discover Schema
Correlations (I)

• Solution find duplicates which use different
  attributes.
• Observation1. 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

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Personalized Search Ranking Discover Schema
Correlations ( II)
duplicates E1, E2, E3, E4, E5,
E6attribute matches title, subject, author,
writer, pub-date, rec-date
duplicates E1, E2, E3, E4, E5,
E6attribute matches title, subject, author,
writer, pub-date, rec-date
Xuan Zhou, Julien Gaugaz, Wolf-Tilo Balke,
Wolfgang Nejdl. Query Relaxation Using Malleable
Schema. SIGMOD07
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Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

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

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

Not a member?
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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
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Privacy Access ControlUse Credentials
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Privacy Access ControlNegotiations
Bob
Alice
Winsborough, Seamons, Jones. Automated trust
negotiation. DARPA Information Survivability
Conference and Exposition, 2000
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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

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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
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Privacy Access ControlSample Screenshot (I)
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Privacy Access ControlSample Screenshot ( II)
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Outline

• L3S Background
• Introduction Motivation
• Personalized Search Ranking
• Privacy Access Control
• EU Projects Summary

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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
Person
friend
Email
Event
Topic
acquaintance
Person
Document
WebSite
colleague
Image
Personal Semantic Web
a semantically enlarged
Social protocols
NEPOMUK enabled
intimate supplement to memory
and distributed search
peers
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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
Integration
High - Impact
Openess Federation
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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

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EU Projects SummaryEU IP TENCompetence
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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

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• Thanks !
• Daniel Olmedilla
• olmedilla_at_L3S.de - http//www.L3S.de/olmedilla/