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Feature Based Approaches to Semantic Similarity

This document discusses feature-based approaches to semantic similarity, comparing metric distance and feature matching methods. Key concepts include the principles of minimality, symmetry, and triangle inequality in the context of similarity. The text addresses the assumptions of independence and monotonicity, examining how the addition of features affects perceived similarity. Case studies involving classification systems, such as the National Vegetation Classification System, illustrate practical applications. The role of context and diagnosticity in assessing similarities among entities is also explored, shedding light on feature value and specificity in matching processes.

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Feature Based Approaches to Semantic Similarity

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

  2. THE BASICS

  3. Why feature based??

  4. Metric Distance vs. Feature Matching • Metric distance: • Minimality = • Symmetry --> = --> • Triangle Inequality --> & --> then --> • Feature Matching • Matching • Monotonicity • Independence

  5. 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

  6. 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

  7. 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

  8. 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??

  9. Feature Similarity and Context The altering of clusters changes the similarity of objects in each cluster- diagnosticity hypothesis

  10. 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??

  11. MEASURING SIMILARITY

  12. LULC systems National Vegetation Classification System Modified Anderson Classification System Elk Habitat Classification System Attributes, Functions and Parts Formation of Universe of Discourse

  13. LULC lessons • Ability for matching is dependent on the need. • Specificity of matches varies by circumstances ( Elk shelter vs. Elk food).

  14. Geospatial Entities • Matching-Distance Similarity Measure Assess Similarity Distance based Feature based Distinguishing Features (attributes, functions, parts) Semantic Structure (is-a, part-whole)

  15. Geospatial Entities • Matching process • Weights defined for the similarity values of parts, functions and attributes • For each type of distinguishing feature,

  16. Applying Weights

  17. Similarity Calculation…

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