Using Concept Lattices for Visual Navigation Assistance in Large

Databases

Application to a Patent Database

Jean Villerd, Sylvie Ranwez, Michel Crampes, David Carteret

LGI2P – École des Mines d’Alès, France I-Nova Company, Villeurbanne – Lyon, France

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Outline

1. Context and Problem Setting

2. State of the Art

3. Objective : Intended Visualization

4. Proposal for a Visual Browsing Method

5. Discussion and Perspectives

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1. Context and Problem Setting

Scalability issuesstorage of new information + 30 % each year [Lyman & Varivan 2003]

Visual scalability“capability of visualization representations and visualization tools to display massive data sets effectively, in terms of either the number or the dimension of individual data elements” [Eick & Karr 2002]

Binary-tree visualization of the Yahoo search engine bot crawling the experimental website

Microsoft Research's Netscan project

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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2. State of the Art

Information Visualization“focus + context” paradigm [Schneiderman 1996]

Collection Visualization collection’s structure

Grokker, Kartoo, TreeMaps semantic distance (PCA, MDS)

Molage

Molage

Grokker

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

our goal: merging both

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FCA for Information Retrieval classification querying and browsing

Credo, MailSleuth, ImageSleuth

ImageSleuth [Eklund, Ducrou et al. 2006]

Can information visualization techniques improvethis lattice-based navigation process?

Credo [Carpineto et al. 2004]

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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3. Objective: Expected Visualization

extract the structure overview the structure focus on a structure element content (local

view) propose navigation paths through the structure

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

structure

semantic distance

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overview : collection of formal concepts

local view : focus on a particular concept :intent = { plasma display, display panel }

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

3. Objective: Expected Visualization

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4. Proposal for a Visual Browsing Method

Boolean features

numerical features

plasma_display

display_panel

plasma

p1 = x1

p2 = x2

p0 = x0

document indexation vector in raw data

document x =

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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document / term matrixresulting lattice

overview : collection of clustersi.e. the concept lattice

local view : one cluster’s content(i.e. one formal concept’s extent)

selectionby user

document / documentdistance matrix

raw data

Boolean featuresnumerical features

4. Proposal for a Visual Browsing Method

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

4.1 Lattice spatialization

Computation of a distance matrix thanks to the distance described in [Ranwez 2006]

Projection using Force Direct Placement method with Molage

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4.1 Lattice spatialization

Neighbours emphasized when selecting a concept Suggesting further navigation paths

Intent and simplified extent cardinal displayed

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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4.2 Document spatialization

FDP using a single distance matrix for all local views

Distance independent from intent’s features

Documents appear and disappear at the same position during navigation

d(o1,o2)

document / documentdistance matrix

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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329 patents indexed by 10 terms

0.8 average term per patent

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plasma_display

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plasma_display

plasma_displaydisplay_panel

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plasma_displaydisplay_panel

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plasma_displaydisplay_paneldisplay_device

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plasma_displaydisplay_device

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Goals Solutions

extract the structureformal concept extraction from indexed documents

overview the structuredisplay the lattice with a semantic distance on edges

focus on a structure element content (local view)

display documents in a concept intent with respect to a semantic distance on their numerical features

propose navigation paths through the structure

emphasize current concept’s neighbours on the overview

4.3 Synthesis

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

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5. Discussion and perspectives

Prototype and test on greater data Improve lattice distance Visual Assistance for Feature Selection

1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives

Using Concept Lattices for Visual Navigation Assistance in Large

Databases

Application to a Patent Database

Jean.Villerd@ema.frSylvie.Ranwez@ema.fr

Michel.Crampes@ema.frDavid.Carteret@i-nova.fr

École des Mines d’Alès – http://www.ema.frI-Nova Company, Villeurbanne – http://www.i-nova.fr