Automatic classification of text databases through query probing
Sponsored Links
This presentation is the property of its rightful owner.
1 / 27

Automatic Classification of Text Databases Through Query Probing PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

Automatic Classification of Text Databases Through Query Probing. Panagiotis G. Ipeirotis Luis Gravano Columbia University Mehran Sahami E.piphany Inc. Search-only Text Databases. Sources of valuable information Hidden behind search interfaces Non-crawlable Example: Microsoft Support KB.

Download Presentation

Automatic Classification of Text Databases Through Query Probing

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Automatic classification of text databases through query probing

Automatic Classification of Text Databases Through Query Probing

Panagiotis G. Ipeirotis

Luis Gravano

Columbia University

Mehran Sahami

E.piphany Inc.

Search only text databases

Search-only Text Databases

  • Sources of valuable information

  • Hidden behind search interfaces

  • Non-crawlable

    Example: Microsoft Support KB

Interacting with searchable text databases

Interacting With Searchable Text Databases

  • Searching: Metasearchers

  • Browsing: Use Yahoo-like directories

  • Browse & search: “Category-enabled” metasearchers

Searching text databases metasearchers

Searching Text Databases: Metasearchers

  • Select the good databases for a query

  • Evaluate the query at these databases

  • Combine the query results from the databases

    Examples: MetaCrawler, SavvySearch, Profusion

Browsing through text databases

Browsing Through Text Databases

  • Yahoo-like web directories:




      Example from

      Computers > Publications > ACM DL

  • Category-enabled metasearchers

    • User-defined category (e.g. Recipes)

Problem with current classification approach

Problem With Current Classification Approach

  • Classification of databases is done manually

  • This requires a lot of human effort!

How to classify text databases automatically outline

How to Classify Text Databases Automatically: Outline

  • Definition of classification

  • Strategies for classifying searchable databases through query probing

  • Initial experiments

Database classification two definitions

Database Classification: Two Definitions

  • Coverage-based classification:

    • The database contains many documents about the category (e.g. Basketball)

    • Coverage: #docs about this category

  • Specificity-based classification:

    • The database contains mainly documents about this category

    • Specificity: #docs/|DB|

Database classification an example

Database Classification: An Example

  • Category: Basketball

  • Coverage-based classification


  • Specificity-based classification

    •, but not

Categorizing a text database two problems

Categorizing a Text Database:Two Problems

  • Find the category of a given document

  • Find the category of all the documents inside the database

Categorizing documents

Categorizing Documents

  • Several text classifiers available

  • RIPPER (AT&T Research, William Cohen 1995)

    • Input: A set of pre-classified, labeled documents

    • Output: A set of classification rules

Categorizing documents ripper

Categorizing Documents: RIPPER

  • Training set: Preclassified documents

    • “Linux as a web server”: Computers

    • “Linux vs. Windows: …”: Computers

    • “Jordan was the leader of Chicago Bulls”: Sports

    • “Smoking causes lung cancer”: Health

  • Output: Rule-based classifier

    • IF linux THEN Computers

    • IF jordan AND bulls THEN Sports

    • IF lung AND cancer THEN Health

Precision and recall of document classifier

Precision and Recall of Document Classifier

During the training phase:

  • 100 documents about computers

  • “Computer” rules matched 50 docs

  • From these 50 docs 40 were about computers

    • Precision = 40/50 = 0.8

    • Recall = 40/100 = 0.4

From document to database classification

From Document to Database Classification

  • If we know the categories of all the documents, we are done!

  • But databases do not export such data!

    How can we extract this information?

Our approach query probing

Our Approach: Query Probing

  • Design a small set of queries to probe the databases

  • Categorize the database based on the probing results

Designing and implementing query probes

Designing and Implementing Query Probes

The probes should extract information about the categories of the documents in the database

  • Start with a document classifier (RIPPER)

  • Transform each rule into a query

    IF lung AND cancer THEN health  +lung +cancer

    IF linux THEN computers  +linux

  • Get number of matches for each query

Three categories and three databases

Three Categories and Three Databases

linux computers


jordan AND bulls sports

lung AND cancer health


Using the results for classification

Using the Results for Classification

We use the results to estimatecoverage and specificity values

Adjusting query results

Adjusting Query Results

  • Classifiers are not perfect!

    • Queries do not “retrieve” all the documents that belong to a category

    • Queries for one category “match” documents that do not belong to this category

  • From the training phase of classifier we use precision and recall

Precision recall adjustment

Precision & Recall Adjustment

  • Computer-category:

    • Rule: “linux”, Precision = 0.7

    • Rule: “cpu”, Precision = 0.9

    • Recall (for all the rules) = 0.4

  • Probing with queries for “Computers”:

    • Query: +linux  X1 matches  0.7X1 correct matches

    • Query: +cpu  X2 matches  0.9X2 correct matches

  • From X1+X2documents found:

    • Expect 0.7 X1+0.9 X2to be correct

    • Expect (0.7 X1+0.9 X2)/0.4 total computer docs

Initial experiments

Initial Experiments

  • Used a collection of 20,000 newsgroup articles

  • Formed 5 categories:

    • Computers (comp.*)

    • Science (sci.*)

    • Hobbies (rec.*)

    • Society (soc.* + alt.atheism)

    • Misc (

  • RIPPER trained with 10,000 newsgroup articles

  • Classifier: 29 rules, 32 words used

    • IF windows AND pc THEN Computers (precision~0.75)

    • IF satellite AND space THEN Science (precision~0.9)

Web databases probed

Web-databases Probed

  • Using the newsgroup classifier we probed four web databases:

    • Cora (

      CS Papers archive (Computers)

    • American Scientist (

      Science and technology magazine (Science)

    • All Outdoors (

      Articles about outdoor activities (Hobbies)

    • Religion Today (

      News and discussion about religions (Society)



  • Only 29 queries per web site

  • No need for document retrieval!



  • Easy classification using only a small number of queries

  • No need for document retrieval

    • Only need a result like: “X matches found”

  • Not limited to search-only databases

    • Every searchable database can be classified this way

  • Not limited to topical classification

Current issues

Current Issues

  • Comprehensive classification scheme

  • Representative training data

Future work

Future Work

  • Use a hierarchical classification scheme

  • Test different search interfaces

    • Boolean model

    • Vector-space model

    • Different capabilities

  • Compare with document sampling (Callan et al.’s work – SIGMOD99, adapted for the classification task)

  • Study classification efficiency when documents are accessible

Related work

Related Work

  • Gauch (JUCS 1996)

  • Etzioni et al. (JIIS 1997)

  • Hawking & Thistlewaite (TOIS 1999)

  • Callan et al. (SIGMOD 1999)

  • Meng et al. (CoopIS 1999)

  • Login