1. ELPUB 2010, Helsinki, Finland1 A Collaborative Faceted Categorization System – User Interactions Kurt Maly; Harris Wu; Mohammad Zubair ; Contact: maly@cs.odu.edu

2. ELPUB 2010, Helsinki, Finland2 Outline Introduction –What is the problem we are addressing? –What is the approach we are taking? Facet schema evolution – user interactions –Schema enrichment –Anomaly detection –Visual schema presentation rearrangement –User feedback on classifications Conclusions –Future improvements

3. ELPUB 2010, Helsinki, Finland3 Introduction - Problem Problem –Navigating a large growing collection of digital objects, particularly non textual collection such as images and photographs Possible Approaches –Categorize and classify the collection manually using human experts Centralized, expensive, single perspective Static, rigid structure; does not evolve –Use of social tagging systems such as flickr.com Low precision and recall, lack of structure in tags, ambiguity and noise in tags

4. ELPUB 2010, Helsinki, Finland4 Introduction - Approach We built a system that improves access to a large, growing collection by supporting users to build a faceted classification collaboratively –Challenge: continuously classify new objects, modify the facet schema, and reclassify existing objects into the modified facet schema –Collaborative Approach: Enable users to collaboratively build a schema with facets and categories, and to classify documents into this schema Needs automated system support to create critical mass and make it easier for users to collaborate

5. The system front page ELPUB 2010, Helsinki, Finland5

6. Facet and Category Enrichment Statistical co-occurrence model –Subsumption parent-child relationship between x and y if all documents tagged with y are also tagged with x for an existing tagword t –identifies all documents with tag t –If these documents have a common category c, the rule of subsumption implies that t is a possible subcategory of c Example CategorySuggested sub-category American Civil Warmilitary life Chinaboxer rebellion ELPUB 2010, Helsinki, Finland6

7. Schema Cleansing Problem: –categories are created under the wrong facet –child categories might represent a broader concept than the parent category Solution: –Use WordNet’s hierarchical relationships among words to detect anomalies ELPUB 2010, Helsinki, Finland7

8. Schema Cleansing Hierarchy in WordNet (hyponymy: known as “is a” relationship) dog, domestic dog, Canis familiaris => canine, canid => carnivore => placental, placental mammal, eutherian, eutherian mammal => mammal => vertebrate, craniate => chordate => animal, animate being, beast, brute, creature, fauna =>... anomaly detection algorithm Category Parent Cat Grandparent Category Problem President Holiday Politics more closely related to grandparent than to parent ELPUB 2010, Helsinki, Finland8

9. Ordering of Schema Display Problem: –significant number of categories are created under a given facet (or another category) –large number of facets are created Solution: –limit number of child categories/facets displayed –configure administratively –order the display by a popularity measure ELPUB 2010, Helsinki, Finland9

10. Ordering of Schema Display Popularity (P) measure –favours the biggest, most used, and fastest growing facets and categories P = 0.5*f(PN*PC)+ 0.5*PR f – normalizing factor PN - total number of items in a category PR - growth rate of a category: number of new (recent) items for a unit of time PC - number of clicks on the category link in the browsing menu over a period of time ELPUB 2010, Helsinki, Finland10

11. Expanding category display using the “more…” link ELPUB 2010, Helsinki, Finland11

12. Limiting category display using the “more…” link ELPUB 2010, Helsinki, Finland12

13. Quality Assessment through User Feedback “thumb-up” and “thumb-down” buttons available for every association –vote up or down for the association between an image and a category based on how relevant and accurate they think it is Value of this explicit feedback determines when a classification can be deleted or, conversely, when it becomes “hard”, i.e., it is confirmed –Action will update the confidence value of an association by increasing or decreasing it by 0.05 based on whether a user believes it is a correct classification or not –confidence value reaches 1.00 -> association is hardened –confidence value falls below a threshold -> association is deleted ELPUB 2010, Helsinki, Finland13

14. Feedback on category associations ELPUB 2010, Helsinki, Finland14

15. ELPUB 2010, Helsinki, Finland15 Conclusions Schema enrichment, cleansing and ordering are effective tools to remedy problems introduced by collaborative schema evolution Future improvements include recording actual administrator actions for training purposes