Jean-Charles LAMIREL, Jieh HSIANG Liu WJ

Jean-Charles Lamirel [email protected] 3rd SFWINRIA-NSC 3rd SFW

Jean-Charles LAMIREL, Jieh HSIANG

Liu WJ

LORIA, Nancy, France

Using a Background Neural Model in a Digital Library


Research areas : Biological-like models for intelligent

information management

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

The CORTEX team


Main themes of researchInterface for personalized access to informationIntelligent multimedia data miningWeb - Documentary database interaction

Collaborations ORPAILLEUR INRIA team, INIST, LaVillette,

NSC Taiwan, industry... European projects: SCHOLNET, EISCTES

The CORTEX information retrieval and discovery activity


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

Some examples of application


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

[email protected]

Presentation summary


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

Basic set of functionalities for information 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 »

Managing different kinds of queries for 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

Limitations of the classicalmethods for information discovery


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

Neural methods for information cartography


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

Butterfly museum application

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


Viewpoint classifications

Global and/or cross viewpoints classifications

Butterfly application

Query by keywords

Query by example

Adding new individuals

User interface

Combination of results

Validation of insertionor classification recalculation

Butterfly application automationU

ser interface


IDF

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

Basic topographic map building

WEIGHTED DESCRIPTION

TEXTURE


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

Competitive learning:

Basic topographic map building


Selection of the winning neuronInfluence on the neigbourhood

Current data(image)

at time T

Competitive learning


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

Map labelization and zoning

THEME

« YELLOW »

MULTIMEDIA THEMATIC CARTOGRAPHY OF « BUTTERFLY »

COLOR VIEWPOINT

THEME

« GREEN »

CENTRAL SUB.

LIST OF THEME MEMBERS

IMAGE DESCRIPTION


VIEWPOINT 1

Basic map (core classification)

On-line generalizations

VIEWPOINT 2

The MultiSOM model


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

Map on-line generalization


Map on-line generalization


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, …

Semantic viewpoints


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

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

WEAK FOCALIZATION STRONG FOCALIZATION

COLOR MAP

BUTTERLIES

Question: Regularities in textures of yellow butterflies ?

Inter-map communicationTEXTURE MAP

Response: YES, Spots and Edges


Compliance with IR operations

Question: Are there butterflies with spots AND veins ?

Response = NOResponse = YES


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

THEME

« YELLOW »

USE OF COLOR PROTOTYPES

COLOR VIEWPOINT

YELLOW


Experimentation on patents (texts)

Goal : Intelligent technological survey =

Full text analysis of the patentsDomain of oil engineeringProvide 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

[email protected]

DILIBReformating

Nominal groupsExtraction

MicroNOMADMultiSOM

Patents in XMLFormat

Structured by Viewpoints

PatentsDatabase

Interactive maps for analysisInteractive maps for analysisValidated

Multi-indexes

ViewpointsDefinition


Nominal groups extraction

1) Lexicographic analysis (compound terms)

2) Normalization :

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

Results :

Déposants Titre Utilisation AvantagesNombre de documents indexés 1000 1000 745 624Nombre d’index bruts générés 73 605 252 231Nombre final d’index (après filtrage) 32 589 234 207Nombre de classes non vides par carte * 28 55 57 61


Patents reindexingSelected Viewpoints: title, use, advantages and patentees

Use

Advantages

Title (Components)

Patentees

Example of dynamic analysisDYNAMIC 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 »

CLASS DESCRIPTION

Classical methods (AK-means)

Hidden link !

CLASSES MAP


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

Conclusion


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:

1) La Vilette: interactive browsing through museum collection, setting up of exibitions

2) INIST: Cartography of the Web (EISCTES EEC Project)

Perspectives


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

Analysis

Jean-Charles LAMIREL,

Yannick TOUSSAINT (Orpailleur)


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 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, I2 there is a unique meet and join.

Inheritance of properties Extraction of association rules:

Search Engine {Web, IR}

{{i1, i2, i3, i4} , }

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

{{i1, i2}, {Web, IR} }{{i4}, {AI, Robots} }

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

R1 = Search Engine {Web, IR}

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}


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

Projection

Agglomeration

3-steps methodologyGrouping


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

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

Multi-classificator cooperation (PhD) SVM Stigmergy Genetic Neural maps

On-line learning of user ’s behaviour, intelligent relevance feedback


Annexes

Topographic inconsistencies Area computation Inter-map communication Activity coherency

Topographic inconsistencies

NO INCONSISTENCIES WEAK INCONSISTENCIES

STRONG INCONSISTENCIES

Neuron neighbourhood

STRONG

GLOBAL

Topographic inconsistencies

Area computation

WHILE IN

DO

END DO

SO AS


HYPERAWACKENESS : CONDITIONNAL POSSIBILITY

WEIGHTED SUM : CONDITIONNAL PROBABILITY

Viewpoint oriented Patents Analysis

Selected Viewpoints: title, use, advantages and patentees

[email protected]

MAP OF VIEWPOINT: ADVANTAGES

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 « friction coefficient» of oil

Documents

Jean-Charles LAMIREL, Jieh HSIANG Liu WJ