Object Fusion in Geographic Information Systems

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Object Fusion in Geographic Information Systems

• Catriel Beeri, Yaron Kanza,
• Eliyahu Safra, Yehoshua Sagiv
• Hebrew University
• Jerusalem Israel

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The Goal Fusing Objects that Represent the Same
Real-World Entity
Example three data sources that provide
information about hotels in Tel-Aviv
MAPI the survey of Israel
MAPA commercial corporation
MUNI The municipally of Tel-Aviv
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The Goal Fusing Objects that Represent the Same
Real-World Entity
MAPI cadastral and building information
MAPA tourist information
MUNI Municipal information
Is there a nearby parking lot?
Hotel Rank
Each data source provides data that the other
sources do not provide
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The Goal Fusing Objects that Represent the Same
Real-World Entity
MAPI cadastral and building information
MAPA tourist information
MUNI Municipal information
Object fusion enables us to utilize the different
perspectives of the data sources
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Why Are Locations Used for Fusion?

• There are no global keys to identify objects that
  should be fused
• Names cannot be used
• Change often
• May be missing
• May be in different languages
• It seems that locations are keys
• Each spatial object includes location attributes
• In a perfect world, two objects that represent
  the same entity have the same location

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Why is it Difficult to use Locations?

• In real maps,
• locations are inaccurate
• The map on the left is an overlay of the three
  data sources about hotels in Tel-Aviv

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Inaccuracy ? Difficult to Use Locations

• It is difficult to distinguish between
• A pair of objects that represent close entities
• A pair of objects that represent the same entity
• Partial coverage complicates the problem

?
1
a
2
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Fusion methods

• Assumptions
• There are only two data sources
• Each data source has at most one object for each
  real-world entity i.e., the matching is
  one-to-one

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Corresponding Objects

• Objects from two distinct sources that represent
  the same real-world entity

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Fusion Sets

• A fusion algorithm creates two types of fusion
  sets
• A set with a single object
• A set with a pair of objects one from each data
  source

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Confidence

• Our methods are heuristics ? may produce
  incorrect fusion sets
• A confidence value between 0 and 1 is attached to
  each fusion set
• It indicates the degree of certainty in the
  correctness of the fusion set

Fusion sets with high confidence

Fusion sets with low confidence

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The Mutually-Nearest Method

• The result includes
• All mutually-nearest pairs
• All singletons, when an object is not part of pair

Finding nearest objects
Fusion sets
input
nearest
nearest
1
a
2
1
a
2
1
a
2
nearest
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The Probabilistic Method

• An object from one dataset has a probability of
  choosing an object from the other dataset
• The probability is inversely proportional to the
  distance

Confidence the probability that the object is
not chosen by any
Confidence the probability of the mutual
choice

A threshold value is used to discard fusion sets
with low confidence
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Mutual Influences Between Probabilities
Case I
1
a
2
1
a
2
0.3
0.2
Case II we expect
1
a
2
1
a
2
b
b
0.05
0.8
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The Normalized-Weights Method

• Normalization
• captures mutual
• influence

Iteration brings to equilibrium
Results are superior to those of the previous
two methods (at a cost of only a small increase
in the computation time)
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Measuring the Quality of the Result
R Fusion sets in the result
E Entities in the world
C Correct fusion sets in the result
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A Case Study Hotels in Tel-Aviv
State of the art
Our three methods
The traditional nearest neighbor (Best results) Mutually nearest Proba-bilistic method Normal-ized weights method
Recall 0.48 0.77 0.80 0.85
Precision 0.56 0.85 0.80 0.90
All three methods perform much better than the
nearest-neighbor method
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• Extensive tests on synthesized data are described
  in the paper

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Conclusions

• The novelty of our approach is in developing
  efficient
• methods that find fusion sets with high recall
  and
• precision, using only location of objects.

Thank you!
You are invited to visit our poster And our web
site http//gis.cs.huji.ac.il/