1. Information Organization: Overview

2. IO: What What is Information Organization?
Systematic arrangement of items
group similar items together
assign meaning to groups
determine relationships between groups
assign items to groups
Grouping 1
Grouping 2
Grouping 3
Big
Small
Square
Circle
Blue
Red
Small
Big
Small
Big
Small
Big
Small
Big
Blue
Red
Square
Circle
Square
Circle
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3. IO: Why Why organize information?
Why do we put certain things in certain places?
Closet
- Seasonal groups - Pants vs. Shirts - Color groups - Favorite vs. non-favorite
To find things easier
→ Information Retrieval (IR)
Taxonomy
Food
Good Bad
sweet taste smell like milk too hot hard to chew
To make sense of the world
→ Knowledge Discovery (KD)
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4. IO: How What to do when information to organize is massive?
How do we organize information?
General Approach
anticipate how item is searched for
e.g. by subject, date, author
look for common features among items
determine what an item is about
Classification
Identification/creation of classes
Assignment of items into classes
Clustering
group similar items together
What to do when information to organize is massive?
10,000 books
100,000 journal papers
1,000,000 web pages
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5. Machine Learning: Introduction
What is Machine Learning?
A computer program learns if it improves its performance at some task through experience (T. Mitchell, 1997).
Any change in a system that allows it to perform better the second time on repetition of the same task or on task drawn from the same population (H. Simon, 1983).
How can systems improve?
By acquiring new knowledge
Acquiring new facts
Acquiring new skills
By adapting its behavior
Solving problems more accurately
Solving problems more efficiently
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6. Machine Learning: Introduction
Which is different?
Which are similar?
How is learning possible?
Because there are regularities in the world.
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7. ML: Classification vs. Clustering
Task is to learn to assign instances to predefined classes
Supervised Learning
data has to specify what we are trying to learn (the classes)
requires training data
predefined classes and classified items
Clustering
Task is to learn a classification from the data
no predefined classification is required
Unsupervised Learning
data doesn’t specify what we are trying to learn (the clusters)
Clustering algorithms divide a data set into natural groups (clusters)
items in the same cluster are similar to each other and share certain properties
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8. IO for IR Clustering Document Clustering Cluster Hypothesis
Documents having similar contents tend to be relevant to the same query
Rank clusters by Query-Cluster Similarity
Cluster documents based on vector similarity
Post-retrieval clustering
Scatter-Gather
Keyword Clustering
Automatic Thesaurus Construction
Query Expansion
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9. IO for IR Classification Document Categorization
classify documents into manually defined categories
supports hierarchical browsing, query expansion via relevance feedback
Document Indexing
assign keywords to documents
automatic indexing with controlled vocabulary, metadata generation
Document Filtering
e.g. news delivery, spam filtering
Query Classification
collection selection
algorithm selection
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