Conferencia Web semantica Mihai Datcu

  • 807 views
Uploaded on

Presentación de Mihai Datcu sobre web semantica, data mining y web 3.0 en la Jornada de Web Semántica organizada por la AEI del Conocimiento de Asturias y el Cluster TIC. …

Presentación de Mihai Datcu sobre web semantica, data mining y web 3.0 en la Jornada de Web Semántica organizada por la AEI del Conocimiento de Asturias y el Cluster TIC.

Celebrada el 4 de junio de 2010.

More in: Technology , Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
807
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Folie 1 1 Remote Sensing Technology Institute Institut für Methodik der Fernerkundung bzw. Deutsches Fernerkundungsdatenzentrum
  • 2. Media semantic content extraction: the perspectives Prof.
Mihai
Datcu

  • 3. The
Data
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Photos
and
hand‐ drawn
images
 Books
 (Corel)
 Tomography
 Video
frames
 Infrared
images
 (Run,
Lola,
Run)
 of
fawns
(DLR)
 Maps
 Seismic
 records
 Satellite
 Genome
 images
 3
  • 4. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 The data archive Past years: Meta-data based file access The data The meta - data
  • 5. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 WWW
 5
  • 6. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Scene Sensor Data, Content and Knowledge 6
  • 7. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Scene Sensor Data, Content and Knowledge Image 7
  • 8. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Scene Sensor How EO is working ? Data, Content and Knowledge Data Image 8 Users
  • 9. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Scene Sensor Data, Content and Knowledge Knowledge Data Image 9 Users
  • 10. Understanding and semantics   Roots
of
understanding
   Content
=>
seman.c
=>
ontology
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

   Grand
challenge
–
interoperability
of
seman.cs
   Communica.on
between
machines
vs
communica.on
between
 individuals
   Syntac.c
metadata
vs
Seman.c
metadata
vs
semio.c
metadata
 (Umberto
Ecco)
   Semio.cs


  • 11. Semantic compositionality: the meaning of a hole is a function of the meanings of its parts and their mode of syntactic combination and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 City factory residence installation car tree hause 11
  • 12. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Symbolic synonymy 12
  • 13. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Symbolic synonymy 13
  • 14. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 Symbolic synonymy 14
  • 15. Data
Mining
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 •  1974 at the Office of Naval Research •  a boiler explosion on a distroyer •  the boiler was the problem for other accidents •  data/information existed, but was ignored •  no tools to find patterns •  R&D program to discover such problems
  • 16. Data
Mining
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 
What
is
data
mining?

 
Data
created
by
people
   A
process
of
sor.ng
through
a
large
amount
of
data
and
picking
out
 relevant
informa.on.
   Data
=>
informa.on
=>
knowledge
=>
ac.onable
intelligence
 (understanding)

  • 17. Data
Mining
 What
is
informa8on
mining?

 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Data
created
by
sensor
   
 Data
understanding
from
Observa.on:
A
contextual
approach

 •  Crea.on
of
contextual
understanding
from
data.
 •  Sensor
Data
=>
models
working
on
data
create
informa.on
 (content)
=>
contextual
knowledge
about
geopoli.cal
and
 socioeconomic
factors
=>
ac.onable
intelligence

at
the
local
level
 (understanding)
   How
does
data
mining
build
knowledge?
 Memory,
communica.on,
paVern
recogni.on
   Challenge
is
to
understand
data
and
to
have
all
data
accessible
 Issues
–
formats,
etc.

  • 18. Features Volume and multimodality of data is growing and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Data and information is spatio-temporal and unstructured Users want to have the knowledge Interactive is the only way of operation Exploration is the predominant mode of interaction Context is critical and relevant Users are interested in information and knowledge dependent of conjecture 18
  • 19. Features and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Information must be obtained from the data Databases and search engines were not designed to provide contents Visualization is very important HMI are crucial Words – signals – semantics Visual – perception – cognition Memory – latency – knowledge - relevance 19
  • 20. Archives and Libraries   Archive: a long-term storage area, place or collection containing records, and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 documents, or other materials of historical interest (that’s passive and static!)   Library: a depository built to contain books and other materials for reading and study (that’s active and dynamic!) What makes the difference?   Library has:   A catalogue (better then archives): indexing books based on multiple criteria, e.g. author, title, keywords, domains (ontology!)...   A librarian: one who has the care of a library and its contents, selecting the books, documents and non-book materials which comprise its collection, and providing information and loan services to meet the needs of its users 20
  • 21. Searching Libraries   Use the catalogue: select indexes and search the books. Next, read them.   Walk trough the library: browse till you get interested... and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

   A friend told me...: go to the material using prior information   Ask the librarian: the ideal librarian, he   reads all the incoming books   interprets contents   associate with other information   creates categories   understand the inquiry   dialogues   comments, and   suggest ... 21
  • 22. INTERNET
SEARCH
ENGINES
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 •  Traditional WEB •  find keywords in documents •  based on HTML •  SEMANTIC WEB •  understand the nature of documents •  based on intelligent agents
  • 23. CONCEPT
DE
COMMUNICATION
AVANCE
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Extraction de Représentation Source l’information sémantique Utilisateurs d’information Inférence du modèle de signal Inférence du modèle objectif d ’information subjectif Modèles Modèles Modèles Modèles stochastiques déterministes syntaxiques sémantiques Modélisation Modélisation de la conjecture du signal utilisateur The principle of semantic compositionality: the meaning of a whole is a function of the meanings of its parts and their mode of syntactic combination 23
  • 24. Knowledge based Image Information Mining TSX Ground Segment Systems Integrated in operational environments DIMS Image Information Mining (IIM) and
Image
Understanding
for
Earth
Observa.on
 technologies for enhanced information Competence
Centre
on
Informa.on
Extrac.on

 content extraction from EO image KIM archives. Operate the new functionalities in the TerraSAR-X Payload GS. Method: Interface of DIMS and KIM systems. Extend the DIMS product catalogue with the semantic and image feature catalogues of KIM. Provide IIM functions. Applications: •  concurrent queries of DIMS and KIM catalogue a •  interactive selection of EO products information content •  IIM functions (explore, semantic annotation, detection-discovery, etc.) •  new generations of GS systems Envisaged missions: •  TerraSAR-X, TanDEM-X •  SRTM Services •  MERIS (SSE) •  GMES Data (EOWEB) Information (KIM)
  • 25. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on

  • 26. Competence
Centre
on
Informa.on
Extrac.on

 and
Image
Understanding
for
Earth
Observa.on
 KIM
CONCEPT:
SIMPLE

  • 27. Today: Interactive, user adapted, EO data content access and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

 Help to image classification Suggest data Mine Fields Access to: information knowledge Knowledge share Help to image understanding
  • 28. Compression‐based
Similarity
Measures
   Most
well‐known:
Normalized
Compression
Distance
(NCD)
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

   General
Distance
between
any
two
strings
x and
y Similarity
metric
under
some
assump.ons
   Basically
parameter‐free
   Applicable
with
any
off‐the‐shelf
compressor
(such
as
Gzip)
   If
two
objects
compress
beVer
together
than
separately,
it
means
they
share
 common
paVerns
and
are
similar
 x Coder C(x) Coder C(xy) NCD y Coder C(y) Li,
M.
et
al.,
“The
similarity
metric”,
IEEE
Tr.
Inf.
Theory,
vol.
50,
no.
12,
2004

 28
  • 29. Applica8ons
of
CBSM
 Clustering
and
classifica8on
of:
 DNA
Genomes
   Texts
 and
Image
Understanding
for
Earth
Observa.on
 Competence
Centre
on
Informa.on
Extrac.on

   Music
   DNA
genomes
   Chain
leVers
 Primates
   Images
   Time
Series
 Rodents
   …
 Satellite
images
 Seismic
signals
 Landslides
 Explosions
 29