The document proposes using concept lattices and information visualization techniques to navigate large databases. It discusses (1) challenges of scalability in data storage and visualization, (2) relevant state-of-the-art in information visualization and formal concept analysis (FCA), and (3) a visual browsing method using FCA and spatialization algorithms to provide overviews and local views of concept lattices representing document collections. The method aims to extract structure from indexed documents and assist navigation through emphasized concept relationships and semantic document maps.

1. Using Concept Lattices for Visual Navigation Assistance in Large Databases Application to a Patent Database Jean Villerd , Sylvie Ranwez, Michel Crampes, David Carteret LGI2P – É cole des Mines d’Alès, France I-Nova Company, Villeurbanne – Lyon, France

2. Outline Context and Problem Setting State of the Art Objective : Intended Visualization Proposal for a Visual Browsing Method Discussion and Perspectives

3. 1. Context and Problem Setting Scalability issues storage 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

4. 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

5. FCA for Information Retrieval classification querying and browsing Credo, MailSleuth, ImageSleuth ImageSleuth [Eklund, Ducrou et al. 2006] Can information visualization techniques improve this 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

6. 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

7. overview : collection of formal concepts 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 local view : focus on a particular concept : intent = { plasma display , display panel }

8. 4. Proposal for a Visual Browsing Method Boolean features numerical features 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 plasma_display display_panel plasma p1 = x1 p2 = x2 p0 = x0

9. overview : collection of clusters i.e. the concept lattice local view : one cluster’s content (i.e. one formal concept’s extent) document / document distance matrix raw data Boolean features numerical 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 document / term matrix resulting lattice selection by user

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

11. 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

12. 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 document / document distance matrix 1. Context and Problem Setting | 2. State of the Art | 3. Expected Visualization | 4. Proposal for a Visual Browsing Method | 5. Perspectives d(o1,o2)

13. 329 patents indexed by 10 terms 0.8 average term per patent

14. plasma_display

15. plasma_display plasma_display display_panel

16. plasma_display display_panel

18. plasma_display display_panel display_device

19. plasma_display display_device

20. 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 formal concept extraction from indexed documents extract the structure emphasize current concept’s neighbours on the overview propose navigation paths through the structure display documents in a concept intent with respect to a semantic distance on their numerical features focus on a structure element content (local view) display the lattice with a semantic distance on edges overview the structure Solutions Goals

21. 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

22. Using Concept Lattices for Visual Navigation Assistance in Large Databases Application to a Patent Database [email_address] [email_address] [email_address] [email_address] É cole des Mines d’Alès – http://www.ema.fr I-Nova Company, Villeurbanne – http://www.i-nova.fr