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Feature Based Approaches to Semantic Similarity Kate Deutsch May 1, 2008

Feature Based Approaches to Semantic Similarity

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Feature Based Approaches to Semantic Similarity. Kate Deutsch May 1, 2008. THE BASICS. Why feature based??. Metric Distance vs. Feature Matching. Metric distance: Minimality = Symmetry --> = --> Triangle Inequality --> & --> then - PowerPoint PPT Presentation

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Page 1: Feature Based Approaches to Semantic Similarity

Feature Based Approaches to Semantic Similarity

Kate Deutsch

May 1, 2008

Page 2: Feature Based Approaches to Semantic Similarity

THE BASICS

Page 3: Feature Based Approaches to Semantic Similarity

Why feature based??

Page 4: Feature Based Approaches to Semantic Similarity

Metric Distance vs. Feature Matching

Metric distance: Minimality = Symmetry --> = --> Triangle Inequality --> & --> then

-->

Feature Matching Matching Monotonicity Independence

Page 5: Feature Based Approaches to Semantic Similarity

Assumptions Examined

Matching Similarity

f(intersection and individual features)

Monotonicity Similarity increases with the addition of

common features and/or deletion of distinct features

Independence

Page 6: Feature Based Approaches to Semantic Similarity

Matching Functions

Contrast Model: Similarity measurement is a linear combination of the measures of common and distinctive parts

Ratio Model: Similarity measurement is constructed from various set theories and normalized

Page 7: Feature Based Approaches to Semantic Similarity

Asymmetry and Focus

Are these the same??? Assess the degree to which a and b are similar to

each other Assess the degree to which a is similar to b

Case studies Countries Figures Letters Signals

Page 8: Feature Based Approaches to Semantic Similarity

What do we do?

“ Nevertheless, the symmetry assumption should not be rejected altogether. It seems to hold in many contexts, and it serves as a useful approximation in many others. It cannot be accepted, however as a universal principle of psychological similarity.”

Can we think of an instance??

Page 9: Feature Based Approaches to Semantic Similarity

Feature Similarity and Context

The altering of clusters changes the similarity of objects in each cluster- diagnosticity hypothesis

Page 10: Feature Based Approaches to Semantic Similarity

Diagnostic Value

“Features that are shared by all objects under consideration cannot be used to classify these objects and are therefore devoid of diagnostic value”

What do you think??

Page 11: Feature Based Approaches to Semantic Similarity

MEASURING SIMILARITY

Page 12: Feature Based Approaches to Semantic Similarity

Modified Anderson

ClassificationSystem

LULC systems

NationalVegetation

ClassificationSystem

Elk HabitatClassification

System

Attributes, Functions and Parts

Formation ofUniverse of Discourse

Page 13: Feature Based Approaches to Semantic Similarity

α

Page 14: Feature Based Approaches to Semantic Similarity

LULC lessons

Ability for matching is dependent on the need. Specificity of matches varies by

circumstances ( Elk shelter vs. Elk food).

Page 15: Feature Based Approaches to Semantic Similarity

Geospatial Entities

Matching-Distance Similarity Measure

Assess Similarity

Distinguishing Features (attributes,

functions, parts)

Semantic Structure (is-a, part-whole)

Feature based Distance based

Page 16: Feature Based Approaches to Semantic Similarity

Geospatial Entities

Matching process Weights defined for the similarity values of

parts, functions and attributes

For each type of distinguishing feature,

Page 17: Feature Based Approaches to Semantic Similarity
Page 18: Feature Based Approaches to Semantic Similarity

Applying Weights

Page 19: Feature Based Approaches to Semantic Similarity

Similarity Calculation…

Page 20: Feature Based Approaches to Semantic Similarity