Probe, Count, and Classify:
Categorizing Hidden Web Databases
Panagiotis G. Ipeirotis
Luis Gravano
Columbia University
Mehran Sahami
E.piphany Inc.
DIMACS Summer School Tutorial on New Frontiers in Data Mining
Theme: Web -- Thursday, August 16, 2001
Surface Web vs. Hidden Web
Keywords
SUBMIT
Surface Web
Link structure
Crawlable
CLEAR
Hidden Web
No link structure
Documents “hidden”
behind search forms
Do We Need the Hidden Web?
Surface Web
2 billion pages
Hidden Web
500 billion pages (?)
Example: PubMed/MEDLINE
PubMed: (www.ncbi.nlm.nih.gov/PubMed) search: “cancer”
 1,341,586 matches
AltaVista: “cancer site:www.ncbi.nlm.nih.gov”
 21,830 matches
Interacting With Searchable Text Databases
Searching: Metasearchers
Browsing: Yahoo!-like web directories:
InvisibleWeb.com
SearchEngineGuide.com
Created
Manually!
Example from InvisibleWeb.com
Health > Publications > PubMED
Classifying Text Databases Automatically:
Outline
Definition of classification
Classification through query probing
Experiments
Database Classification: Two Definitions
Coverage-based classification:
Database contains many documents about a category
Coverage: #docs about this category
Specificity-based classification:
Database contains mainly documents about a category
Specificity: #docs/|DB|
Database Classification: An Example
Category: Basketball
Coverage-based classification
ESPN.com, NBA.com, not KnicksTerritory.com
Specificity-based classification
NBA.com, KnicksTerritory.com, not ESPN.com
Database Classification: More Details
Thresholds for coverage and specificity
Tc: coverage threshold (e.g., 100)
Ts: specificity threshold (e.g., 0.5)
Ideal(D): set of classes for database D
Class C is in Ideal(D) if:
D has “enough” coverage and specificity
(Tc, Ts) for C and all of C’s ancestors
and
D fails to have both “enough” coverage
and specificity for each child of C
Tc, Ts
“editorial”
choices
Ideal(D)
SPORTS
C=800
S=0.8
BASKETBALL
S=0.5
Root
HEALTH
C=200
S=0.2
BASEBALL
S=0.5
From Document to Database Classification
If we know the categories of all documents inside the
database, we are done!
We do not have direct access to the documents.
Databases do not export such data!
How can we extract this information?
Our Approach: Query Probing
1.
Train a rule-based document classifier.
2.
Transform classifier rules into queries.
3.
Adaptively send queries to databases.
4.
Categorize the databases based on
adjusted number of query matches.
Training a Rule-based Document Classifier
Feature Selection: Zipf’s law pruning, followed by
information-theoretic feature selection [Koller & Sahami’96]
Classifier Learning: AT&T’s RIPPER [Cohen 1995]
Input: A set of pre-classified, labeled documents
Output: A set of classification rules
IF linux THEN Computers
IF jordan AND bulls THEN Sports
IF lung AND cancer THEN Health
Constructing Query Probes
Transform each rule into a query
IF lung AND cancer THEN health  +lung +cancer
IF linux THEN computers  +linux
Send the queries to the database
Get number of matches for each query, NOT the
documents (i.e., number of documents that match each rule)
These documents would have been classified by the rule under
its associated category!
Adjusting Query Results
Classifiers are not perfect!
Queries do not “retrieve” all the documents in a category
Queries for one category “match” documents not in this
category
From the classifier’s training phase we know its “confusion
matrix”
Confusion Matrix
Correct class
comp
10% of “Sports” classified as “Computers”
sports health
comp
0.70
0.10
0.00
sports
0.18
0.65
0.04
health
0.02
0.05
0.86
Probing results
DB-real
X
1000
5000
50
=
700+500+0 = 1200
180+3250+2 = 3432
20+250+43 = 313
10% of the 5000 “Sports” docs to “Computers”
Classified into
M . Coverage(D) ~ ECoverage(D)
Confusion Matrix Adjustment:
Compensating for Classifier’s Errors
comp
DB-real
1000
5000
50
=
sports health
comp
0.70
0.10
0.00
sports
0.18
0.65
0.04
health
0.02
0.05
0.86
-1
Probing results
X
1200
3432
313
M is diagonally dominant, hence invertible
Coverage(D) ~ M-1 . ECoverage(D)
Multiplication better approximates the correct result
Classifying a Database
1.
2.
3.
4.
Send the query probes for the top-level categories
Get the number of matches for each probe
Calculate Specificity and Coverage for each category
“Push” the database to the qualifying categories
(with Specificity>Ts and Coverage>Tc)
5.
6.
Repeat for each of the qualifying categories
Return the classes that satisfy the coverage/specificity
conditions
The result is the Approximation of the Ideal classification
Real Example: ACM Digital Library
(Tc=100, Ts=0.5)
Root
Arts
(0,0)
Software
(2060, 0.355)
Computers
(9919, 0.95)
Hardware
(2709, 0.465)
C/C++
Health
(0,0)
Science
(430, 0.042)
Programming
(1042, 0.18)
Perl
Java
Visual Basic
Sports
(22, 0.008)
Experiments: Data
72-node 4-level topic hierarchy from InvisibleWeb/Yahoo! (54
leaf nodes)
500,000 Usenet articles (April-May 2000):
Newsgroups assigned by hand to hierarchy nodes
RIPPER trained with 54,000 articles (1,000 articles per leaf)
27,000 articles used to construct estimations of the confusion matrices
Remaining 419,000 articles used to build 500 Controlled Databases
of varying category mixes, size
Comparison With Alternatives
DS: Random sampling of documents via query probes
Callan et al., SIGMOD’99
Different task: Gather vocabulary statistics
We adapted it for database classification
TQ: Title-based Probing
Yu et al., WISE 2000
Query probes are simply the category names
Experiments: Metrics
Expanded(N)
Accuracy of classification results:
Expanded(N) = N and all descendants
Correct = Expanded(Ideal(D))
Classified = Expanded(Approximate(D))
N
Precision = |Correct /\ Classified|/|Classified|
Recall = |Correct /\ Classified|/|Correct|
F-measure = 2.Precision.Recall/(Precision + Recall)
Cost of classification: Number of queries to database
Average F-measure, Controlled Databases
1.0
0.9
F-measure
0.8
0.7
0.6
0.5
DS [Callan et al.]
PnC
TQ [Yu et al.]
0.4
0.3
0
0.1
0.2
0.3
0.4
0.5
Ts, (Tc=8)
0.6
0.7
0.8
0.9
1
PnC =Probe & Count, DS=Document Sampling, TQ=Title-based probing
Experimental Results: Controlled Databases
Feature selection helps.
Confusion-matrix adjustment helps.
F-measure above 0.8 for most <Tc, Ts> combinations.
Results degrade gracefully with hierarchy depth.
Relatively small number of probes needed for most
<Tc, Ts> combinations tried.
Also, probes are short: 1.5 words on average; 4 words
maximum.
Both better performance and lower cost than DS
[Callan et al. adaptation] and TQ [Yu et al.]
Web Databases
130 real databases classified from InvisibleWeb™.
Used InvisibleWeb’s categorization as correct.
Simple “wrappers” for querying (only # of matches needed).
The Ts, Tc thresholds are not known (unlike with the
Controlled databases) but implicit in the IWeb categorization.
We can learn/validate the thresholds (tricky but easy!).
More details in the paper!
Web Databases: Learning Thresholds
0.8
0.7
0.5
0.4
0.3
0.2
0.1
0
0.9
0.6
0.3
262144
0
65536
16384
4096
1024
Tec
256
64
16
4
1
F-measure
0.6
Tes
Experimental Results:
Web Databases
130 Real Web Databases.
F-measure above 0.7 for best <Tc, Ts> combination
learned.
185 query probes per database on average needed
for classification.
Also, probes are short: 1.5 words on average; 4 words
maximum.
Conclusions
Accurate classification using only a small number of
short queries
No need for document retrieval
Only need a result like: “X matches found”
No need for any cooperation or special metadata from
databases
URL: http://qprober.cs.columbia.edu
Current and Future Work
•
Build “wrappers” automatically
•
Extend to non-topical categories
•
Evaluate impact of varying search interfaces (e.g., Boolean
vs. ranked)
•
Extend to other classifiers (e.g., SVMs or Bayesian models)
•
Integrate with searching (connection with database
selection?)
Questions?
Contributions
Easy, inexpensive method for database classification
Uses results from document classification
“Indirect” classification of the documents in a database
Does not inspect documents, only number of matches
Adjustment of results according to classifier’s performance
Easy wrapper construction
No need for any metadata from the database
Related Work
Callan et al., SIGMOD 1999
Gauch et al., Profusion
Dolin et al., Pharos
Yu et al., WISE 2000
Raghavan and Garcia Molina, VLDB 2001
Controlled Databases
500 databases built using 419,000 newsgroup articles
One label per document
350 databases with single (not necessarily leaf) category
150 databases with varying category mixes
Database size ranges from 25 to 25,000 articles
Indexed and queries using SMART
F-measure for Different Hierarchy Depths
1.00
0.95
0.90
F-measure
0.85
0.80
0.75
0.70
0.65
0.60
DS [Callan et al.]
PnC
TQ [Yu et al.]
0.55
0.50
0
1
Level
2
3
PnC =Probe & Count, DS=Document Sampling, TQ=Title-based probing
Tc=8, Ts=0.3
Query Probes Per Controlled Database
Interactions with Database
2000
1750
1500
1250
PnC
DS
TQ
1000
750
500
0
0.1
0.2
0.3
0.4
0.5
Tes
0.6
0.7
0.8
0.9
1
(a)
Interactions with Database
2000
1750
1500
1250
PnC
DS
TQ
1000
750
500
250
1
2
4
8
16
32
64
Tec
(b)
128
256
512
1024
2048
4096
8192
Web Databases: Number of Query Probes
600
400
300
200
100
1
4
16
64
256
1024
4096
16384
65536
262144
0.9
0.6
Tes
0.3
0
0
Tec
Query Probes
500
3-fold Cross-validation
0.9
0.8
0.7
F-1
F-2
F-3
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
13 6
10
7
26 2
21
4
52 4
42
88
53
8
65
76
4
32
38
16
92
81
96
40
48
20
2
24
10
51
6
25
8
12
64
32
16
8
4
2
1
0
Real Confusion Matrix for
Top Node of Hierarchy
Health
Sports
Science
Computers
Arts
Health
0.753
0.018
0.124
0.021
0.017
Sports
0.006
0.171
0.021
0.016
0.064
Science
0.016
0.024
0.255
0.047
0.018
Computers
0.004
0.042
0.080
0.610
0.031
Arts
0.004
0.024
0.027
0.031
0.298