Jong Y. Choi, Joshua Rosen, Siddharth Maini, Marlon E. Pierce, and Geoffrey C. Fox

Community Grids LaboratoryIndiana University

Delicious example

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Bookmark

Tags

SocialNetwork

s

People-generate

d

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

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4

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Search ResultSOAP, REST, …

Repository

Query with various options

RDFRSSAtomHTML

Populate Bookmarks/ tags

Distributed Tagging Data

CCT System

Data Coordinator

User Service

Data Importer

Collective Collaborative Tagging (CCT) System

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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

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Different Data Sources

Various IR algorithms

Flexible Options

Result Comparison

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Searching

Given input tags, returning the most relevant X (X = URLs, tags, or users)

Latent Semantic Indexing (LSI), FolkRank

IRecommendation

Indirect input tags, returning undiscovered XII

Clustering

Community discovering. Finding a group or a community with similar interests

K-Means, Deterministic Annealing Clustering

IIITrend detection

Analysis the tagging activities in time-series manner and detect abnormality

Time Series AnalysisIV

Service Description Algorithm

Type

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

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10(Source : MSI-CIEC)

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

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

11Precision

Reca

ll

2 terms4 terms8 terms20% dim. reductionNone

Ideal Line

Discover the group structures of URLs Non-parametric learning algorithm

Non-trivial optimization problem Should avoid local minima/maxima solution

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Deterministically avoid local minima Tracing global solution by changing level of

energy Analogy to physical annealing process (High

Low)

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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, …

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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

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-. Distances, cosine, …-. O(N2) complexity

Dis-similarity

Vector-space Model

-. Paths, hops, connectivity, …-. O(N3) complexity

Graph Model

-. Latent Semantic Indexing-. Dimension reduction schemes-. PCA

Algorithm-. PageRank, FolkRank, …-. Pairwise clustering-. MDS

-. q-dimensional vector-. q-by-n matrix

Represen-tation

-. G(V, E) -. V = {URL, tags, users}

Pairwise clustering Input from vector-based model vs. graph

model How to avoid local minima/maxima? (e.g, K-

Means)

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Graph modelVector-space model