1. Extracting Schema from Semistructured Data
Nestorov, Abiteboul, and Motwani at Stanford

2. Perspective This paper is new work.
More than the details look at the issues:
What are their goals?
What does this contribute?
Do they attain their goals?
Why do we need this?

3. Sample Database 24 “The Keg” “Steak” “Jim” “Burger King” “Fries” “AA+7 1
Hours
Manager
Name
Manager
Entree
Entree
Name 8 9 10 11 2 3 4 24
“The Keg”
“Steak”
“Jim”
“Burger
King”
“Fries”
Company
Name
Phone 5 6
“AA+
Management”
Schema = Types

4. Where does semistructured data come from?
Document collections
Biological data
HTML
Bibtex, etc.

5. Who needs structure? For the user Storage
To know what queries are possible
Browsing the database
Type checking
Storage
Data layout to facilitate querying
E.g. place similar objects on same page
Indexes

6. Who Needs Structure?(2) Query optimization Other?
All the relational query optimization tricks
Maintaining statistics per data type
Cardinality, # of pages, Index cardinality, etc.
Estimating the cost/size of result of query plans
Efficient processing of path expressions
Other?

7. Example (little lie) Typing Program:
Their Goals
Approximate typing (schema extraction) of
semistructured data.
Example (little lie) Typing Program:
Restaurant(X) :- Link(X,A,B,C) & Name-atom(A) &
Entrée-atom(B) & Manager-atom(C)

8. Outline of the Algorithm
Given a database:
1. Find the perfect typing program.
This typing might be too large so we:
2. Coalesce similar types into k types.
3. Assign a type to objects in database.
4. Deduce meaningful names for the types.

9. Typing The two base relations: - link(FromObj, ToObj, Label)
- atomic(Obj, Value) 7
Manager
Name
Entree 8 9 10
“The Keg”
“Steak”
“Jim”
These are the only two EDB’s of the typing program.
Restaurant(X) :- link(X,A,Name) & atomic(A, Ap) &
link(X,B,Entrée) & atomic(B, Bp) &
link(X,C,Manager) & atomic(C,Cp)

10. Typing 2 Restaurant(X) :- link(X,A,Name) & atomic(A, Ap) &
link(X,B,Entrée) & atomic(B, Bp) &
link(X,C,Manager) & atomic(C,Cp)
EDB:
link(7, 8, Name) atomic(8, “The Keg”)
IDB: (intensional relations)
defined by the typing program
Extension of an IDB:
Restaurant(1)

11. Restriction on Types Arbitrary type programs are not allowed.
Rules typei(X) can only be built from the following:
1. link(Y, X, c) & typej(Y)
2. link(X, Y, c) & typej(Y)
3. link(X, Y, c) & atomic(Y, Z)
Types can only express local characteristics.
The collection of typed links is a set.
(2 entrées = 1 entrée) cj cj c0 X

12. Semantics of Type Program
The greatest fixpoint of a datalog program on a database defines the semantics of the typing.
Fixpoint = Extensions of IDB’s + EDB’s
Least fixpoint
start with model of only EDB’s
at each step union into the model anything new.

13. Greatest Fixpoint
1. Start with a model of EDB’s and all possible extensions.
2. At each step, remove any extensions not derived by applying
the rules.
Least fixpoint doesn’t work:
person(X) :- link(X, Y, is-manager-of) & firm(Y) &
link(X, Yp, name) & atomic(Yp, Z)
firm(X) :- link(X, Y, is-managed-by) & person(Y) &

14. Imperfect Types Defect: a measure of how well an
object fits a given type.
= Excess + deficit
type1 = +
Defect is 2 for assigning 11
to type1. 7
Manager
Name
Entree 4 5 6
“The Keg”
“Steak”
“Jim”
manager0
name0
entree0 11
# seats
Name
Entree 8 9 10
“McD”
“biscuit” 53

15. Imperfect Types(2)
Excess: # of EDB’s not used to validate any object’s type.
Deficit: Minimum # of ground facts that need to be added to make all type derivations possible. 7
Manager
Name
Entree 4 5 6
“The Keg”
“Steak”
“Jim” 11
# seats
Name
Entree 8 9 10
“McD”
“biscuit” 53

16. Perfect Typing Program (Stage 1)
Gore.

17. Multiple Roles How hard is it to choose to types for the cover?
Country
Country
Movie
Name
Team
Team
Movie
Name
Movie
France
Rocky
Horror
Name
Scholes
Man Utd
Bleu
Star Trek
Country
Binoche
Cantona
England
How hard is it to choose to types for the cover?
How do you quantify atomization?

18. Clustering (Stage 2) Define a distance function between two types:
First approximation is difference between the bodies of
their rule definitions.
t1 :- a0, b2 t2 :- a0, b1
t3 :- b2, b1, b3
d(t1, t2) = 2

19. A Better Function Include some measure of the weight of a type(# of
objects of that type):
t2 ~> t1
Some desirable properties:
increasing in d = coalesce similar types
decreasing in w1 = compensate for ‘expected noise’
increasing in w2 = maintain types with large extents
Choosing what to coalesce is hard!

20. Recasting (Stage 3)
Assign each object to types within the k types formed
from stage 2.
(optional) choose a better value of k an rerun step 2.

21. Results Heavy use of synthetic data. What do the graphs show?
Create a type definition and generate instances that are peturbed randomly in some way.
What do the graphs show?
Are the data sets realistic?

22. Conclusions Paper problems:
The algorithm isn’t completely explained.
Many comments are not elaborated.
But, it’s an important problem and good first approach.