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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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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
-->
Feature Matching Matching Monotonicity Independence
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
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
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
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??
Feature Similarity and Context
The altering of clusters changes the similarity of objects in each cluster- diagnosticity hypothesis
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??
MEASURING SIMILARITY
Modified Anderson
ClassificationSystem
LULC systems
NationalVegetation
ClassificationSystem
Elk HabitatClassification
System
Attributes, Functions and Parts
Formation ofUniverse of Discourse
€
α
LULC lessons
Ability for matching is dependent on the need. Specificity of matches varies by
circumstances ( Elk shelter vs. Elk food).
Geospatial Entities
Matching-Distance Similarity Measure
Assess Similarity
Distinguishing Features (attributes,
functions, parts)
Semantic Structure (is-a, part-whole)
Feature based Distance based
Geospatial Entities
Matching process Weights defined for the similarity values of
parts, functions and attributes
For each type of distinguishing feature,
Applying Weights
Similarity Calculation…