BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction - PowerPoint PPT Presentation

Loading...

PPT – BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction PowerPoint presentation | free to view - id: 1141dd-YTkwM



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction

Description:

BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction ... Ontology population from multimedia content. ... – PowerPoint PPT presentation

Number of Views:115
Avg rating:3.0/5.0
Slides: 29
Provided by: dal78
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction


1
BOEMIE Bootstrapping Ontology Evolution with
Multimedia Information Extraction
  • Vasileios Papastathis
  • Centre for Research and Technology Hellas (CERTH)
  • Informatics and Telematics Institute
  • Multimedia Knowledge Group
  • 3rd Know-How Transfer Event
  • Thessaloniki, 8 March 2007

2
Presentation Overview
  • A short presentation of CERTH-ITI and the
    Multimedia Knowledge Group (MKG)
  • BOEMIE project An FP6 success story
  • FP7 Challenge 4 Digital Libraries and Content

3
History-Scope
  • Founded in 1998 as a non-profit organisation
    under the auspices of the General Secretariat of
    Research and Technology of the Greek Ministry of
    Development
  • Since March 2000, it is part of the Centre for
    Research and Technology Hellas as one of its
    four constituent institutes
  • Set-up to constitute a major research and
    development centre, with continuous interaction
    with the academic community, the National and
    European Informatics and Telematics Industry, the
    international scientific community and the Public
    Sector
  • Play a key role in the development of the Greek
    Information Society as a National Center of
    Excellence in Informatics and Telematics
  • Set-up spin-off companies aiming at the
    commercial exploitation of ITIs research results

4
Structure and Organization
  • Virtual Reality Research Unit
  • Advanced e-Services for the Knowledge Society
    Research Unit
  • Telecommunications and Telematics Research Unit
  • Intelligent Systems and Software Engineering
    Research Unit
  • Business Information Systems Research Unit

5
Structure and Organization
  • Multimedia Knowledge Group
  • Semantic Multimedia Analysis
  • Multimedia Indexing and Retrieval
  • Multimedia and the Semantic Web
  • Knowledge Structures, Languages and Tools for
    Multimedia
  • Reasoning and Personalization for Multimedia
    Applications
  • MPEG-7 and MPEG-21 Standards

6
Personnel
  • 13 Professors
  • 7 Researchers Grade C and D
  • 5 Post-Doctoral Researchers
  • 20 PhD Candidates
  • (Postgraduate Research Fellows)
  • 45 Research Assistants
  • (all University Graduates, MSc)
  • 2 Technicians (University Graduates)
  • 3 Administration Staff
  • 20 Undergraduate Students

7
Publications and Projects
  • Since 2000
  • 140 publications in peer-reviewed international
    journals
  • 46 book chapters
  • 370 publications in international and national
    conferences
  • More than 450 citations (not by authors or
    coauthors)
  • 70 RD projects funded by European Commission
    Programmes (8.8 MEuro)
  • 31 RD projects funded by National Programmes
    (2.2 MEuro)
  • 50 Industrial Contracts and Subcontracts (2.45
    MEuro)
  • 27 5th FP European projects
  • Coordinator in 4 EC IST projects (SCHEMA NoE,
    LAURA, EU-PUBLI.COM, KOD Knowledge on Demand)
  • Financial coordinator of 2 EC IST projects
    (INTERVUSE, P2People)

8
Funding (2002-2006)
9
FP6 RD Projects
  • aceMedia Integrating knowledge, semantics and
    content for user centred intelligent media
    services, IP 2004-2007.
  • KnowledgeWeb Realizing the Semantic Web,
    funded by the DG XIII, NoE 2004-2007.
  • MESH Multimedia Semantic Syndication for
    Enhanced News Services, IST IP, 2006-2008.
  • X-Media Knowledge Sharing and Reuse Across
    Media, IST IP, 2006-2009.
  • BOEMIE Bootstrapping Ontology Evolution with
    Multimedia Information Extraction, IST-STREP,
    2006-2008.
  • K-Space Knowledge Space of Semantic Inference
    for Automatic Annotation and Retrieval of
    Multimedia Content, 6th FP IST NoE, 2006-2008.
  • Since 2000
  • 106 publications in peer-reviewed international
    journals
  • 46 book chapters
  • 342 (32616) publications in international and
    national conferences
  • More than 450 citations (not by authors or
    coauthors)
  • 40 RD projects funded by European Commission
    Programmes (8.8 MEuro)
  • 31 RD projects funded by National Programmes
    (2.2 MEuro)
  • 50 Industrial Contracts and Subcontracts (2.45
    MEuro)

10
BOEMIE Bootstrapping Ontology Evolution with
Multimedia Information Extraction
11
Specific Targeted Research Projects (STREP)
  • Aims and Objectives
  • An RTD project designed to gain knowledge or
    improve existing products, processes or services
  • A demonstration project designed to prove the
    viability of new technologies, but which cannot
    be commercialized directly
  • Number of participants
  • Minimum of 3 partners from three different Member
    States
  • Duration
  • Typically between 2 to 3 years
  • Projects Management
  • Require overall management and coordination of
    the consortium

12
The facts
  • Specific Targeted Research Projects (STREP), IST
    2004 2.4.7 Semantic-based Knowledge and
    Content Systems
  • Start March 1, 2006
  • End February 28, 2009
  • Budget 5.075.678 Euro
  • EU Funding 3.150.000 Euro
  • More than 30 people already active in the project
  • Project portal http//www.boemie.org/

13
Consortium
  • Inst. of Informatics Telecommunications, NCSR
    Demokritos, Greece (Coordinator)
  • Fraunhofer Institute for Media Communication
    (NetMedia), Germany
  • Dip. di Informatica e Comunicazione, University
    of Milano, Italy
  • Centre for Research and Technology Hellas (CERTH)
    - Informatics Telematics Institute (ITI),
    Greece
  • Hamburg University of Technology, Germany
  • TeleAtlas SA, the Netherlands

14
Vision
  • Pave the way towards automation of the knowledge
    acquisition from multimedia content.
  • Break new ground by introducing and implementing
    the concept of evolving multimedia ontologies.
  • Make domain-specific semantic webs feasible with
    limited human effort.

15
Objectives
  • Providing technology to represent and evolve
    domain-specific multimedia ontologies.
  • Moving from low-level, general-purpose,
    single-modality feature extraction towards
    semantic, multimedia analysis.
  • Robust and scalable ontology-driven multimedia
    content extraction through ontology evolution.

16
Approach
  • Driven by domain-specific multimedia ontologies,
    BOEMIE information extraction systems will be
    able to identify high-level semantic features in
    image, video, audio and text and fuse these
    features for optimal extraction.
  • The ontologies will be continuously populated and
    enriched using the extracted semantic content.
  • This is a bootstrapping process, since the
    enriched ontologies will in turn be used to drive
    the multimedia information extraction system.

17
Approach
F
V
T
A
SEMANTICS EXTRACTION TOOLKIT
V
VISUAL EXTRACTION TOOLS
T
TEXT EXTRACTION TOOLS
ONTOLOGY EVOLUTION PROCESS
EVOLVED ONTOLOGY
A
AUDIO EXTRACTION TOOLS
INFORMATION FUSION TOOLS
F
18
Semantics extraction Objectives
  • No single modality is powerful enough to support
    robust and large-scale extraction.
  • Emphasis on fusion of multiple modalities, using
    reasoning and uncertainty handling.
  • Contribution to the state-of-the-art in visual
    content analysis, due to its richness and the
    difficulty of extracting semantics.
  • Non-visual content will provide supportive
    evidence, to improve precision.

19
Multimedia semantic model Objectives
  • A multimedia ontology describes the structure of
    multimedia content and visual characteristics of
    content objects in terms of low-level features.
  • One or more domain ontologies, e.g. about
    athletics.
  • A geographic ontology, e.g. about landmarks.
  • An event ontology, e.g. about athletic events.
  • Potential contribution
  • Uncertainty in concept descriptions
  • Spatial and temporal relations

20
Ontology evolution Objectives
  • Ontology population and enrichment, i.e. addition
    of concepts, relations, properties and instances.
  • Coordination of homogeneous ontologies (same
    domain) and heterogeneous ontologies (e.g. domain
    and multimedia ontologies).
  • Potential contribution
  • Ontology population from multimedia content.
  • Coordination of different types of reasoning for
    enrichment and coordination.
  • Matching, coordination and versioning of the
    integrated semantic model.

21
7th FP
22
Challenge 4
  • Digital Libraries and Content
  • Make content and knowledge abundant, accessible,
    interactive and usable over time by humans and
    machines alike.
  • Content must be made available through digital
    libraries and its long term usability,
    accessibility and preservation must be ensured
  • Effective technologies need to be developed for
    intelligent content creation and management, and
    for supporting the capture of knowledge and its
    sharing and reuse
  • Individuals, organisations and communities must
    find new ways to acquire and exploit knowledge,
    and thereby learn
  • Political framework  i2010 - Digital
    Libraries 

23
Intelligent Content Semantics
  • Make digital resources that embody creativity
    and semantics easier and more cost effective to
    produce, organize, search, personalise,
    distribute and use across the value chain.
  • CREATORS Design more communicative and
    participative forms of content (media
    professionals, enterprise designers, talented
    amateurs).
  • PUBLISHERS Increase productivity in creative
    industries, enterprises and professional sectors
    (e.g. health, law, etc.).
  • SCIENTISTS Automate link between data analysis,
    theory and experimental validation.
  • ORGANISATIONS COMMUNITIES Automate collection
    and distribution of digital content and
    machine-tractable knowledge, and their sharing in
    collaborative environments.

24
Target socio-economic sectors
  • key features
  • ICT based, high growth innovation potential
  • pronounced international character
  • sophisticated users
  • very large data volumes
  • well defined flows protocols
  • obvious candidates (in addition to ICT!)
  • creative industries (film, TV, games, advertising
    …)
  • enterprises in information bound industries
  • utilities eg energy
  • manufacturing process industries
  • construction engineering, financial services …
  • eScience eg life sciences

25
Do NOT do
  • In 2007-08 NO intend to support research into
  • basic research with no identifiable by-products
    within 10 years
  • domain specific applications - not
    portable/replicable in other socio-economic
    sectors
  • developments addressing immediate commercial
    imperatives (e.g. content protection
    monetisation)
  • issues covered by other Challenges and Objectives
    eg media networking, peer to peer, technology
    enabled learning …
  • topics well covered by on-going FP6 projects
    networks (see our website)

26
Schedule of 1st call (provisional)
  • 51 Meuro in total of which
  • 46 Meuro for IP STR projects
  • 5 Meuro for NoEs CSAs
  • first call expected to close late April (?)
  • evaluation/selection mid-May late Jun (?)
  • negotiations until Nov
  • contract awarding in Dec
  • projects due to start Q1 2008
  • highly demanding process …

27
CERTH-ITI in FP7
  • Continue research in Multimedia and Knowledge
    Technologies
  • Expand to new areas and applications (Health,
    Industry, Cognition, Robotics, Environment,
    Security, Surveillance, …)
  • Challenges in IST
  • Networked media
  • Cognitive systems, interaction, robotics
  • Digital libraries and technology-enhanced
    learning
  • Intelligent content and semantics
  • Personal health systems for monitoring and
    point-of-care diagnostics
  • Advanced ICT for risk assessment and patient
    safety

28
Thank you! Mr. Vasileios Papastathis
vkpapa_at_iti.gr Multimedia Knowledge
Group http//mkg.iti.gr
About PowerShow.com