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Presented by Kaverappa Kallangada

Determining Semantic Similarity among Entity Classes from Different Ontologies M. Andrea Rodriguez Max J. Egenhofer. Presented by Kaverappa Kallangada. LAYOUT Introduction Prior Work Approach Experiments Conclusions Future Work. INTRODUCTION Ontology A schema or a model

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Presented by Kaverappa Kallangada

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  1. Determining Semantic Similarity among Entity Classes from Different OntologiesM. Andrea RodriguezMax J. Egenhofer Presented by Kaverappa Kallangada

  2. LAYOUT • Introduction • Prior Work • Approach • Experiments • Conclusions • Future Work

  3. INTRODUCTION Ontology • A schema or a model • Describes the types in a domain • Describes the relationships between types • Describes the direction of relationship Example: • Domain -> Class -> Person, Project, Course • Types -> Object -> John, Project 1 , CS 586 • Relationship -> Connects classes-> worksOn, takes • Constraints -> Defines the direction of relationship -> People work on Projects.

  4. PRIOR WORK • Earlier work depended on integrated ontology • Ontology integration is expensive • Ontology inconsistencies and mismatches needs to be handled • The proposed approach assess similarity independent ontologies • Based on matching process proposed by Tversky et al. • Exploits several ontology specifications

  5. APPROACH Entity Classes • Concepts that group entities or objects of real world into classes • Examples are buildings ( house, office, school etc), automobiles ( car, SUV, truck, bike etc) Entity Class representation 3 basic components for the representation of entity classes • A set of synonym words (synset) that denotes an entity class • A set of semantic inter-relations among these entity classes • A set of distinguishing features that characterize entity classes

  6. 1. Synset – Set of synonyms • Addresses polysemy and synonymy Example: The use of word bank 2. Set of Semantic interrelations • Hyponymy and Meronymy play a vital role • Hyponymy is “is-a” relation • Specific  General concept • Hyponymy is transitive and asymmetric • Meronymy is a part-whole relation • Is it transitive? - May be

  7. 3. Set of Distinguishing features • Organization of entity classes given by semantic interrelation • Hard to distinguish entity classes that have the same super class • Example: Hospital and Bungalow Finer identification of distinguishing features Features are classified into 3 parts • Functions: What is done with the instance of the class? • Parts: Structural elements of the class - Can have items that are not in entity class - Example: roof, floor etc. - Is part-whole relation not applicable here? • Attributes: Additional characteristics of the entity class

  8. Comparing Entity classes • Can be done only if the entity classes share some components • Three different assessments 1. Similarity of Synonym sets • Similarity between an entity class and itself is maximal • Exploit the general consensus on use of words across ontologies • Very basic and inconclusive Example: Clinic and Hospital 2. Semantics as a measure of distinguishing feature • Determines how similar (or not) the entity classes are semantically • Context dependent and asymmetric

  9. 3. Semantic relations • Compares semantic relations between target and base class Example: Two or more entity classes having the same super class (House and Hospital) • Compares semantic neighborhood Semantic neighborhood (N) of an entity class • set of entity classes whose distance to the ‘target’ entity class is less than or equal to the radius (r) of semantic neighborhood. Distance between entity classes • Number of arcs along the shortest path • Distance to itself is zero • Example: semantic neighbors of Stadium with the semantic radius of 1 are: stadium, Structure, Athletic field and Sports Arena

  10. Integrating similarity • Integrate information obtained from 3 assessments. for • ‘a’ is an entity class in Ontology P and ‘b’ is an entity class in Ontology Q • Sw, Su and Sn : Semantic similarity of synsets, features and semantic neighborhood. • ωW, ωu and ωn: Weights of similarity for each specification • ωW+ ωu + ωn =1

  11. A matching approach to similarity assessment • Earlier model was based on semantic relation • Matching model doesn’t follow minimality, symmetry and transitive property • Example: Office building and building • ‘a’ (target) and ‘b’ (base) are entity classes • A and B are description sets of a and b • α(a, b) is the relative importance of non common characteristics for 0≤α≤1

  12. Relative importance of non common characteristics Word net Ontology SDTS Ontology Connected independent ontology

  13. Relative importance of non common characteristics (Contd.) • Allows asymmetric evaluation • Common features between classes vary • Roots of separate ontologies are connected to a imaginary parent “anything” • Depthis a function that returns number of arcs in the shortest path from the entity class to the imaginary root “anything” • Depth of building in Wordnet ontology is 5 and 2 in SDTS • α= 2/(2+5) = 0.28 • 0<α<= 0.5 indicates that the entity classes are same depth

  14. Word matching (Sw) • Checks the number of common and different words in synsets • Example: Building in Wordnet ontology and SDTS ontology • α is 0.28 == 0.58 Feature matching (Su) • Sp, Sf, Sa : Similarity of parts, functions and attributes. • ωp, ωf and ωa : weights of similarity of parts, functions and attributes. • By default , ωp = ωf = ωa • Applies strict string matching • Features match if the intersection of their synset is not empty

  15. Stadium (WS) Entity_class:{ name: { stadium, bowl, arena} description: large structure in which athletic events are held is_a: {construction*} part_of: { } whole_of: { athletic_field} parts:{ {athlectic_field,ports_field,playing_field}, {dressing_room}, { foundation}, {midfield}, {stands}, {ticket_office, box_office, ticket_booth} } functions: { {play, compete}, {play, practise}, {recreate, play} } attributes: { {architectural_property}, {covered/uncovered}, {name}, { lighted/unlighted}, {owner_type},{user_type},{sports_type} } } Stadium (WordNet) Entity_class:{ name: { stadium, bowl, arena} description: large structure in which athletic events are held is_a: {construction*} part_of: { } whole_of: { athletic_field} parts:{ {athlectic_field, sports_field, playing_field}, { foundation}, {midfield}, {plate}, {sports_arena, field_house}, {stands} , {structural_elements}, {standing_room}, {tiered_seats} } functions: { } attributes: { } }

  16. Example: Su(stadiumw,stadiumws)= Sp(stadiumw,stadiumws) for 0≤α≤1 X = stadiumws:parts ∩ stadiumw:parts = {{athletic field, playing field, field},{foundation}, {midfield},{stands}} |X| = 4 Y = stadiumw:parts - stadiumws:parts | Y| = 5 = ˆ{{plate},{sports area, field house}, {standing room}, {structural elements}, {tiered seats}} Z = stadiumws:parts - stadiumw:parts | Z| = 2 = {{dressing room},{ticket office, box office, ticket booth}} =

  17. EXPERIMENTS Cross Ontology Evaluations • Compare computational similarity and answers of human subject test • Three ontologies – WordNet, SDTS and WS Two types of experiments • Searching for equivalent entity class across ontologies • Used in ontology integration • Most similar classes are best candidates for integration • Ranking the similarity between an entity class in one class and a set of entity classes in a second ontology. Example: Search for stadium yields athletic field, football field • Used in information retrieval

  18. Chooses most similar entity class pair - Building vs ( Building, Building Complex) yields Building • When first ontology is a superset of second ontology • Model finds entity classes of second ontology in first ontology

  19. Results of Experiment 1 • Similarity model is highly sensitive to components of entity class • Feature alone isn’t a good measure of similarity • Synonym set and semantic neighborhood are more stable

  20. Experiment 2: Rank of similarity • Compares an entity class in a ontology with a set of entity classes in another ontology • Transformed into rank of similarity • Result of human subject test Stadium  Sports arena, ball park, athletic field, tennis court, theater, museum, building, commons, library, house and transportation • Types of weight settings

  21. SDTS (Stadium) – WS evaluation The ordering of entity classes along the horizontal axis corresponds to the subjects’ responses in decreasing order

  22. Wordnet (Stadium) – WS evaluation • WS(Stadium) – WS evaluation

  23. Correlation coefficient of similarity ranks • Estimated using Spearman rank estimation • WS-WS yields best results . WHY?? • SDTS – WS yields worst results • In experiment 1 ωw=0.5 and ωn=0.5 yield best results. Why not here? • Because with these weights the model finds most similar entity classes only. Results • Feature matching • is important for detecting similar entity classes with in an ontology • detects semantically similar classes across ontologies • Assignments of weights depends on the goal of the assessment • Integration vs retrieval

  24. Conclusions • Similarity model • Provides systematic way to detect similar entity classes • Exploits all components of entity class representation • Is effective as a first step in ontology integration • Synset and semantic neighborhood are effective in detecting similar entity classes • Distinguishing features are effective in tangentially similar entity classes Future work • Compare semantic similarity among features Example: Compare parts semantically • Exploit the semantic relation between the words • Comparison of the set of possible values of attributes for each domain

  25. Thank you Questions?

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