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Using a Background Neural Model in a Digital Library. Jean-Charles LAMIREL, Jieh HSIANG Liu WJ. LORIA, Nancy, France. The CORTEX team. Research areas : Biological-like models for intelligent information management Applications : Autonomous robotics and in-board intelligence

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jean charles lamirel jieh hsiang liu wj

Using a Background Neural Model in a Digital Library

Jean-Charles LAMIREL, Jieh HSIANG

Liu WJ

LORIA, Nancy, France

slide2

The CORTEX team

Research areas :Biological-like models for intelligent information management

Applications :

  • Autonomous robotics and in-board intelligence
  • Numerical classification (vs. symbolical)
  • Information retrieval and discovery
slide3

The CORTEX information retrieval and discovery activity

  • Main themes of research

Interface for personalized access to information

Intelligent multimedia data mining

Web - Documentary database interaction

  • Collaborations
    • ORPAILLEUR INRIA team, INIST, LaVillette, NSC Taiwan, industry...
    • European projects: SCHOLNET, EISCTES
slide4

Some examples of application

  • Adaptive environment for assistance to investigation on the Web
  • Multi-topographic navigation MultiSOM
    • For multimedia data mining
    • For data mining on full text (patents)
  • Numerical-symbolic collaboration
presentation summary
Presentation summary
  • Introduction:
    • Basic set of functionalities for information discovery
    • Limitations of the classical methods for information discovery
  • The MultiSOM model + Butterfly application:
    • Basic behaviour
    • Extensions
  • Management of textual information

lamirel@loria.fr

basic set of functionalities for information discovery
Basic set of functionalities for information discovery
  • Synthetical view of the studied domain =
    • Distribution of the thematical indicators of the domain
    • Highligting of regularities / weak signals
    • Management of several type of synthesis
  • Interactivity =
    • Dynamic data mixture / type of need
    • Choice of meta-orientation of investigation
    • Setting of the granularity level of the analysis
  • Multimedia
managing different kinds of queries for discovery
Managing different kinds of queries for discovery
  • Exploratory (no goal): « Which is the contents of the database ?»
  • Thematic (general orientation): « Images of space conquest »
  • Connotative (hidden goal, indirect research): « Impressive images on human technology »
  • Precise: « Images of Amstrong moonwalk, July 69 »
limitations of the classical methods for information discovery
Limitations of the classicalmethods for information discovery
  • Overall view of the studied domain =
    • Noise
    • Complex interpretation (hidden information)
  • Local views necessarily independant
  • Weaks signal difficult to highlight
  • No interactivity =
    • Passive classification
    • Predefined ways to access to information
neural methods for information cartography
Neural methods for information cartography
  • Topographic learning (SOM) =
    • classification
    • projection
  • Multi-viewpoint modelization capabilties (MultiSOM)
  • Intuitive auto-organization of information
  • Active maps (IR + Navigation)
  • Low human intervention during construction
  • Multimedia capabilities
slide10

Butterfly museum application

  • Different kinds of query
    • Query by keywords
    • Query by example
  • Different kinds of criteria
    • Colour (automatic)
    • Shape (manual)
    • Texture (manual)
  • Problems
    • Hand-made classifications
    • Combination of results coming from different criteria

Yellow = very strong,Red = not,Edge = strongSpot = middle, …

butterfly application automation

Query

by keywords

Query

by example

Adding new

individuals

Butterfly application automation

Global and/or cross viewpoints classifications

User interface

Combination of results

User interface

Validation of insertion

or classification recalculation

Butterfly application

Viewpoint classifications

slide12

WEIGHTED DESCRIPTION

IDF

TEXTURE

Basic topographic map building

  • Data description:
    • Document (image) = index vector : eg vector of characteristics
    • Weighting of the characteristics modalities (very strong=1, …)
    • Optionnal IDF weighting (weak signals detection)
slide13

Basic topographic map building

  • Map predefined parameters settings:
    • Number of neurons
    • Structure : eg 2D grid with square neighbourhood
  • Competitive learning:
slide14

Current data

(image)

at time T

Selection of the winning neuron

Influence on the neigbourhood

Competitive learning

slide15

Map labelization and zoning

  • Map labelization:
    • Based on the best components of the profiles
    • Class or member-oriented
    • One single method is not sufficient

=>Gives an overview of the detected themes

  • Map zoning:
    • Based on the SOM topographic properties
    • Based on the best components of the class profiles

=>Gives an overview of the weights of the themes

slide16

MULTIMEDIA THEMATIC CARTOGRAPHY OF « BUTTERFLY »

THEME

« YELLOW »

CENTRAL SUB.

IMAGE DESCRIPTION

THEME

« GREEN »

LIST OF THEME MEMBERS

COLOR VIEWPOINT

the multisom model

On-line generalizations

Basic map (core classification)

VIEWPOINT 2

VIEWPOINT 1

The MultiSOM model
slide18

Map on-line generalization

  • Goal:
    • Synthethize the map contents by decreasing the number of neurons (classes)
  • Constraints:
    • Preserve the map topographic properties
    • No classification re-computation
  • Method:
    • Exploitation of the neighbourhood relations on the map
slide21

Semantic viewpoints

  • Subspace of the description space
  • Can be a field, a subset of keywords, ...
  • Possible overlapping sets
  • Concurrent or complementary viewpoints

=>Examples: indexer keywords, title keywords, authors, … , visual characteristics, sounds

=>Butterflies: color, shape, texture, …

slide22

Inter-map communication

  • Goal:
    • Cope with the limitations of a global map
    • Allow communication between viewpoints
  • Constraints:
    • Interpretable behaviour
  • Method:
    • Re-projected data = Transmitters neurons
    • Two steps:

1) Activation of a source map (directly or through a query)

2) Transmission to target maps

slide24

Inter-map communication

  • A function:
  • Two modes:
    • Possibilistic (weak thematic relations over viewpoints) 
    • Probabilistic (mesure of the themes similarities)

=> g = class belonging degree

activity coherency
Activity coherency

STRONG FOCALIZATION

WEAK FOCALIZATION

inter map communication

TEXTURE MAP

COLOR MAP

Response: YES, Spots and Edges

Question: Regularities in textures of yellow butterflies ?

Inter-map communication

BUTTERLIES

slide27

Compliance with IR operations

Response = YES

Response = NO

Question: Are there butterflies with spots AND veins ?

remaining problems to be solved
Remaining problems (to be solved)
  • Validation of the automatic classification results by the experts
  • Testing of different results merging methods
  • Test the use of prototype features in classification*
  • Realization of a Web interface for the maps
  • Compare map build-in result combination mechanism with external combination mechanism
  • Test map capabilities for the help in adding new individuals
  • Introduce textual data and combine it with images
slide29

USE OF COLOR PROTOTYPES

THEME

« YELLOW »

YELLOW

COLOR VIEWPOINT

experimentation on patents texts
Experimentation on patents (texts)

Goal : Intelligent technological survey =

Full text analysis of the patents

  • Domain of oil engineering
  • Provide answers to questions like :

1.“Which are the relationships between patentees ?”,

2. “On which specific technology does a patentee work ? Which are the advantages of this specific technology ? For which use ?”,

basic experimental protocol

ViewpointsDefinition

Basic experimental protocol

PatentsDatabase

DILIBReformating

Patents in XMLFormatStructured by Viewpoints

Nominal groupsExtraction

ValidatedMulti-indexes

Interactive maps for analysis

MicroNOMADMultiSOM

lamirel@loria.fr

nominal groups extraction
Nominal groups extraction

1) Lexicographic analysis (compound terms)

2) Normalization :

Ex: “ oil fabrication ” and “ oil engineering” => “ oil engineering ”

  • Results :
patents reindexing
Patents reindexing

Selected Viewpoints: title, use, advantages and patentees

example of dynamic analysis

Title (Components)

Use

Patentees

Advantages

Example of dynamic analysis

DYNAMIC DEDUCTION : Parentee «TONEN CORP. » is a specialist of lubrification of the « automatic transmission ». It products mainly oils based on « organo- molybdenum compound » whic have the specific property of having a « friction coefficient stable stable on a wide range of temperature »

slide36

Conclusion

  • Different viewpoints yield complementary results:
    • Ex: Indexer keywords = Closed themes, Title keywords = Open themes, ...
  • Detection of indexation inconsistencies
  • Projection of thematic pertinence of a query
  • Bilateral synergy: images <=> textual information
  • Very rich and flexible inter-map communication mechanism:
    • Cross analysis between viewpoints, dynamics
    • No limitation regarding viewpoints type and number
slide37

Perspectives

  • Sophisticated 2D mapping, 3D mapping
  • Pure image mosaic navigation
  • Automatization of communication between viewpoints
  • Interaction with Gallois lattice: map zoning and generalization, rule mapping, lattice entry points selection
  • Applications:
  • La Vilette: interactive browsing through museum collection, setting up of exibitions
  • INIST: Cartography of the Web (EISCTES EEC Project)
3 combining symbolic and numeric techniques for dl contents classification and analysis

3) Combining Symbolic and Numeric Techniques for DL Contents Classification and Analysis

Jean-Charles LAMIREL,

Yannick TOUSSAINT (Orpailleur)

introduction
Introduction
  • Combining numerical and symbolic methods:
    • MicroNOMAD Self Organizing Maps (SOM)
      • Basic SOM topographic properties
      • MicroNOMAD multi-map communication process
    • Lattice
      • Formal properties and symbolic deduction
      • Hierarchical structure and inheritance of properties
    • Study of projection of SOM over lattice
      • Making explicit formal properties on the map
      • Map intelligent zoning and labelization
galois lattice
Galois lattice
  • Symbolic hierarchical method: ({i1, i2}, {p1, p2, p3})
  • Partial order defined by the subsumption relation over the set of formal concepts:

(I1, P1)  (I2, P2)  I1 I2,

(I1, P1)  (I2, P2)  P1 P2,

 I1, I2there is a unique meet and join.

  • Inheritance of properties
  • Extraction of association rules:

Search Engine  {Web, IR}

slide41

I = {i1, i2, i3, i4}, P = {AI, Robots, Search Engine, Web, IR}

i1 = {Web, IR}

i2 = {Web, IR}

i3 = {Web, IR, Search Engine}

i4 = {AI, Robots}

{{i1, i2, i3, i4} ,  }

{{i1, i2}, {Web, IR} }

{{i4}, {AI, Robots} }

{{i1, i2, i3}, {Search Engine, Web, IR} }

{ , {IA, Robots, Search Engine, Web, IR} }

R1 = Search Engine  {Web, IR}

complementarity of approaches
Complementarity of approaches
  • Kohonen SOM
    • Complex weighting scheme
    • Difficulty for precise interpretation
    • Good illustrative power (topographic structure)
    • Good synthesis capabilities
    • Non linearity
  • Lattice
    • High number of classes
    • Memory and time consuming
    • Hierarchical structure
    • Rule extraction
    • Incrementality
3 steps methodology

Projection

Grouping

3-steps methodology

Agglomeration

conclusion
Conclusion
  • Cosine method seems to be the best of the test
    • Good accuracy
    • Well-balanced agglomeration
    • Agglomeration preserves closed areas on SOM
  • Other projection and agglomeration methods have to be tested
    • Preservation of partial order and inheritance
perspectives
Perspectives
  • Evaluation on large corpus + Expert
  • Rule management
    • class quality evaluation
    • class labelisation
  • Deduction validation on communicating maps (lattice extensions)
  • Implementation of an operational prototype
other approaches
Other approaches
  • Multi-classificator cooperation (PhD)
    • SVM
    • Stigmergy
    • Genetic
    • Neural maps
  • On-line learning of user ’s behaviour, intelligent relevance feedback
annexes
Annexes
  • Topographic inconsistencies
  • Area computation
  • Inter-map communication
  • Activity coherency
topographic inconsistencies
Topographic inconsistencies

NO INCONSISTENCIES

WEAK INCONSISTENCIES

STRONG INCONSISTENCIES

topographic inconsistencies49
Topographic inconsistencies

GLOBAL

STRONG

Neuron neighbourhood

area computation
Area computation

WHILE

SO AS

IN

DO

END DO

viewpoint oriented patents analysis
Viewpoint oriented Patents Analysis

Selected Viewpoints: title, use, advantages and patentees

lamirel@loria.fr

slide53

Themes «extending oil live » and « black sludge control »are strongly linked together

because they are neighbours on the map

« black sludge » apparition has a negative incidence on the « frictioncoefficient» of oil

MAP OF VIEWPOINT: ADVANTAGES