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.

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
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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
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6. Competence Centre on Informa.on Extrac.on
and Image Understanding for Earth Observa.on
Scene
Sensor
Data, Content and Knowledge
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7. Competence Centre on Informa.on Extrac.on
and Image Understanding for Earth Observa.on
Scene
Sensor
Data, Content and Knowledge
Image
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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
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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
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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
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12. Competence Centre on Informa.on Extrac.on
and Image Understanding for Earth Observa.on
Symbolic synonymy
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13. Competence Centre on Informa.on Extrac.on
and Image Understanding for Earth Observa.on
Symbolic synonymy
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14. Competence Centre on Informa.on Extrac.on
and Image Understanding for Earth Observa.on
Symbolic synonymy
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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
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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
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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
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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 ...
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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
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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
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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
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