1. Jong Y. Choi, Joshua Rosen, Siddharth Maini, Marlon E. Pierce, and Geoffrey C. Fox Community Grids Laboratory Indiana University Collective Collaborative Tagging System

2. People-Powered Knowledge • Delicious example Bookmark Tags Social Networks People-generated

3. People-Powered Knowledge • Collaborative Tagging • Online bookmarking with annotations • Create social networks • Utilize power of people’s knowledge • Pros and cons • High-quality classifier by using human intelligence • But lack of control or authority

4. Motivations • Distributed and fragmented knowledge Need an unified data set More accurate and richer information • No flexibility in choosing different information retrieval (IR) algorithms Need a playground to do experiment with various IR techniques  Help to discover hidden knowledge

5. Proposed System Collective Collaborative Tagging (CCT) System CCT System Data Importer RDF RSS Atom HTML Data Coordinator Distributed Tagging Data Populate Bookmarks/ tags Repository Query with various options User Service Search Result SOAP, REST, …

6. Development Plan and Progress • 1st - Service and algorithm development • Identify services and algorithms • 2nd - Interface development • Web2.o style interface • REST, SOAP, … • 3rd – Export/import service development • Merging distributed data sets • Export data to build mesh-up sites • So far, we are mainly in 1st stage and do some experiments in 2nd stage

7. Prototype Different Data Sources Various IR algorithms Flexible Options Result Comparison

8. Service Types and Algorithms Type Service Description Algorithm I Searching Given input tags, returning the most relevant X (X = URLs, tags, or users) Latent Semantic Indexing (LSI), FolkRank II Recommendation Indirect input tags, returning undiscovered X III Clustering Community discovering. Finding a group or a community with similar interests K-Means, Deterministic Annealing Clustering IV Trend detection Analysis the tagging activities in time-series manner and detect abnormality Time Series Analysis

9. Data Models (I) • Vector-space model (bag-of-words model) • Assume n URLs and q tags • A URL can be represented by q-dimension vector, di = (t1, t2, … , tq) • A total data set can be represented by n-by-q matrix • Pairwise Dissimilarity Matrix • n-by-n symmetric matrix • Distance (Euclidean, Manhattan, … ) • Angles, cosine, sine, … • O(n2) complexity

10. Data Models (II) • Graph model • Building a graph with nodes and edges • Edges are indicating relationship • Becoming complex networks (tag graph) • Dissimilarity • Related with path distance • Finding path is important (Shortest path problem) • Naive approach : O(n3) complexity (Source : MSI-CIEC)

11. Searching • Latent Semantic Indexing • Using vector-space model, find the most similar URLs with user’s query tags • Dimension reduction from high q to low d (q >> d) • Removing noisy terms, extracting latent concepts Ideal Line Recall 2 terms4 terms8 terms20% dim. reductionNone Precision

12. Clustering • Discover the group structures of URLs • Non-parametric learning algorithm • Non-trivial optimization problem • Should avoid local minima/maxima solution

13. Deterministic Annealing Clustering • Deterministically avoid local minima • Tracing global solution by changing level of energy • Analogy to physical annealing process (High  Low)

14. More Machine Learning Algorithms • Classification • To response more quickly to user’s requests • Training data based on user’s input and answering questions based on the training results • Artificial Neural Network, Support Vector Machine,… • Trend Detection • Can be used for prediction/forecasting • Time-series analysis of tagging activities • Markov chain model, Fourier transform, …

15. Conclusion • The goal of our Collective Collaborative Tagging (CCT) system • Utilize various data sets • Provide various information retrieval (IR) algorithms • Help to utilize people-powered knowledge • Currently various models and algorithms are being investigated • Service interfaces and import/export function will be added soon

16. Thank you!! Questions? jychoi@cs.indiana.edu

17. Vector-space Vs. Graph Vector-space Model Graph Model Represen-tation -. q-dimensional vector -. q-by-n matrix -. G(V, E) -. V = {URL, tags, users} Dis-similarity -. Distances, cosine, … -. O(N2) complexity -. Paths, hops, connectivity, … -. O(N3) complexity Algorithm -. Latent Semantic Indexing -. Dimension reduction schemes -. PCA -. PageRank, FolkRank, … -. Pairwise clustering -. MDS

18. Pairwise Dissimilarity • Pairwise clustering • Input from vector-based model vs. graph model • How to avoid local minima/maxima? (e.g, K-Means) Vector-space model Graph model