Ontologies and Similarity

Transcription

1 Web Science & Technologies University of Koblenz Landau, Germany Ontologies and Similarity Acknowledgements to Claudia d Amato, Univ Bari, & WeST Team

2 Agenda Motivation Kris: Brocoli is vegetable used in stir fry What are example semantic applications? Foundation What is an ontology? Reality Check What are typical ontologies? Survey How is similarity measured in ontologies? Critique What should be measured? Solution A preliminary solution Conclusion What to do now? 2

3 Motivation SEMANTIC APPLICATIONS Check out: 3

4 Linked Data Cases with Metadata without Frontiers 4

5 Semantic Search & Browsing: Semantic Portals [WWW 2000] 5

6 Faceted Semantic Media Browsing: Semaplorer Winner Billion Triples Challenge 2008 [JoWS 2009] 6

7 Semantic Desktop Additional Semantic Meta Data, e.g. sender, subject Access to further PIM tools 7

8 Mobile Exploration of Linked Data: Mobile Facets 8

9 Lessons Learned Examples + Semantic Boolean Search in Conjunction with Keyword Search dominates in Ontology-based applications Linked data applications Feast or famine Further use of similarity Learning Ontology engineering advice Available IR Ranking (Textual) Similarity Needed Semantic Ranking Semantic Similarity [Franz et al 09] [stuff here], BUT 9

10 Foundation WHAT IS AN ONTOLOGY? 10

11 What is an ontology? What for? 1. Agreements that make linked data more useful 2. Reasoning Gruber 1993: An ontology is an explicit specification of a conceptualization Oberle, Guarino, Staab. What is an ontology? Handbook on ontologies, Springer

12 Observations in the Real World 12

13 A Model of the Real World Researcher(I046758) cooperates knows knows knows Manager(I034820) Employee(I050000) Researcher(I044443) 13

14 Abstracting from the Individual Model knows knows Researcher cooperates Manager knows Employee Researcher 14

15 A Conceptual Model Intensional Relations Unary Manager Research Employee Binary cooperates knows Cognitive Bias Perception Knowledge Belief The conceptual model captures what is invariant according to one s conceptualization of the world 15

16 Formal Specification What makes it so hard to formally specify ontological commitment? Algebraic Relations do not work: Defined extensionally E.g. Lecturer1 = {Ashwin, Nirmalie, Steffen, Kris, } Problem: New instance would change the ontology, e.g. Lecturer2 = Lecturer1 {Fernando} Intensional Relations need to be defined in Higher Order Language: Specify the intended models where one may quantify over sets of individuals An ontology is a theory (typically in first order logical language) where the possible models approximate the intended models as good as possible 16

17 Conceptualization Perception Reality relevant invariants across presentation patterns: D, State of affairs State of Presentation affairs patterns Phenomena Language L Ontological commitment K (selects D D and ) Models M D (L) Bad Ontology Interpretations I ~Good Ontology Intended models for each I K (L) Ontology models 17 Slide by Nicola Guarino

18 Description Logics: First order language(s) for ontology A-Box Describing Relations Extensionally T-Box Describing Relations Intensionally Flight(LH123). Flight Service. Flight(BA121). Flight to.airport Airport(FRA). Flight to.airport from(lh123,fra). Flight from.airport to(lh123,lhr). Flight from.airport approachedby to -1 Key Feature: Classes (unary FlightFromDE relations) are = Flight defined by relations to definitions from.(airport of other classes part.{de}) 18

19 Description Logics: First order language(s) for ontology A-Box Describing Relations Extensionally T-Box Describing Relations Intensionally Flight(LH123). Flight Service. Flight(BA121). Flight to.airport Airport(FRA). Flight to.airport from(lh123,fra). Flight from.airport to(lh123,lhr). Flight from.airport domain(to) Flight Typically decidable and intractable FlightFromDE = Flight Pragmatically tractable for 10 from.(airport 5 concepts part.{de}) Often most useful at design time only 19

20 Reality Check WHAT ARE TYPICAL ONTOLOGIES? 20

21 Examples for Ontologies & Thesauri Foundational Model of Anatomy 78K classes in FMA 2.0 Several translations to OWL for discovering modeling problems ([Noy & Rubin; Bodenreider et al]) SNOMED CT (Systematized Nomenclature of Medicine -- Clinical Terms) Representation in description logics language EL classes Dewey Decimal System Internationally used thesaurus for forming pre-coordinated classes from an inventory of codes 21

22 Example from Dewey Decimal 770 Photography, Computer Art 590 Animals (Zoology) Photography of Animals Serpentes Photography of Snakes Core message of this talk: Concepts are defined based on the relationship to the definition of other concepts affecting similarity Influencing also non-owl ontologies/thesauri 22

23 Survey HOW IS SIMILARITY MEASURED IN ONTOLOGIES? 23

24 Example Ontology Service Airport Europe Hub part part part Flight LHR LCY FCO FRA DE IT UK part part to to to FRA-LHR FRA-LCY FRA-FCO Including invariant A-Box facts (like Airport(FRA)) 24

25 Similarity Measurement Tasks Comparing Classes Comparing Objects Based on object features Based on class comparisons Comparing Ontologies Lexeme comparisons Graph comparison Considering the semantics of hierarchies isa part Other relations Based on Class comparisons Related to Ontology learning Ontology alignment 25

26 Class Comparisons in Materialized Hierarchies Service Airport Europe part part part Flight LHR LCY FCO FRA DE IT UK part part to to to FRA-LHR FRA-LCY FRA-FCO 26

27 Class Comparisons in Materialized Hierarchies Service Airport Europe part part part Flight LHR LCY FCO FRA DE IT UK part part Flight-DE-UK Flight-DE-IT to to to FRA-LHR FRA-LCY FRA-FCO How many yellow concepts? Infinitely many in powerful DL languages 27

28 Intensional Counting of Path Length Service, ~ , ~ Flight Flight-DE-UK Flight-DE-IT 3 important observations: Most papers investigate dampening, i.e. higher links indicate more dissimilarity Absolute similarity values mostly irrelevant (like in CBR) Most information in the ontology will be discarded FRA-LHR FRA-LCY FRA-FCO [Rada et al.'89] ff 28

29 Intensional Counting of Path Length Service Flight, ~, ~ min 2, min 4,2 1 2 Flight-DE-UK Flight-DE-IT FlightFromHub FlightToHub FlightFrom+ToHub FRA-LHR FRA-LCY FRA-FCO 29

30 `Improved Intensional Counting of Path Length Service,, ~ Flight, ~ 5 9 Further dampening possible Flight-DE-UK Flight-DE-IT FlightFromHub FlightToHub FlightFrom+ToHub FRA-LHR FRA-LCY FRA-FCO 30

31 `Improved Intensional Counting of Path Length - Jaccard Service,, ~ Flight, ~ 5 9, ~ 4 8 Flight-DE-UK Flight-DE-IT FlightFromHub FlightToHub FlightFrom+ToHub FRA-LHR FRA-LCY FRA-FCO 31

32 Intension based Similarity Measurement Strengths Works somehow Weaknesses Both path counting/cotopy heavily suffer from modelling artefacts in the ontology 32

33 Counting Extensions Jaccard-like Metrics Service, ~ 3 6 Flight, ~ 0 4 Flight-DE-UK Flight-DE-IT FlightFromHub FlightToHub FlightFrom+ToHub FRA-LHR FRA-LCY FRA-FCO Disjointness incompatibility LH123 LH127 BA121 BA124 LH567 LH345 AI234 [Resnik 95-99] 33

34 Extension based Similarity Strengths Counting extensions seems natural and efficient (Jaccard-like measure) Weaknesses Disjointness Incompatibility Classes are similar, but do not share instances: Male Female Housecat Lion Extensions are uncountable Ontologies supposed to abstract from specific extensions! Extensions may be infinite 34

35 Class Syntax based Similarity Quite frequent in the literature Listed here just for sake of completeness, because Class syntax based similarity is equivalence unsound 35

36 Critique WHAT SHOULD SIMILARITY DELIVER? [d Amato et al 2008] 36

37 Core criteria for similarity measures almost unchanged 1. Positiveness: C,D sim(c,d) 0 2. Strong reflexivity: C sim(c,c) = 1 3. Upper bound: C,D sim(c,d) 1 4. Symmetry: C,D sim(c,d) = sim(d,c) Problem with strong reflexivity: FlightFromDEHub = Flight from.(hub part.{de}) FromHubAndFromDE = from.hub from. part.{de} Reasoning is needed to discover that sim(flightfromdehub,fromhubandfromde) = 1 37

38 Additional Ones in Ontologies! 5. Prevent Disjointness Incompatibility (seen before) 6. Equivalence Soundness: C,D,E D E sim(c,d)=sim(c,e) Example: sim(flight,flightfromdehub) = sim(flight,fromhubandfromde) Proposition: Reflexivity and triangle inequality imply equivalence soundness 38

39 Additional Ones in Ontologies! 7. Monotonicity a. C L, D L, C U, D U, b. E U, E L c. H such that C H, E H, D H sim(c,d) sim(c,e) U L C D E My feeling is: we need more! (continuity, ) 39

40 Solution A PRELIMINARY SOLUTION [d Amato et al 2010] 40

41 Core idea: Combine Cotopy & Extension-based Approaches Cotopy-based: Intersection at the LeastCommonSubsumer Extension-based: Count instances (or subclasses) Venn diagrams indicates: sim(c,d) > sim(c,e) gcs(c,d) E C D C gcs(c,e) 41

42 Indirect (tentative) Indication of Correctness Growing indexing tree by clustering with new similarity measure Comparing querying time for different ontologies using the original hierarchy and the indexing tree derived from similarity measure Ontology Original hierarchy Clustered tree University 6552 ms 2262 ms Wine 333 ms 268 ms SWSD 235 ms 324 ms Financial ms 6123 ms Problem: similarity computation too expensive [d Amato et al 2010] 42

43 Conclusion WHAT TO DO NOW? 43

44 Conclusion: A call to arms! Semantic applications cover many domains of commercial and social interest Ontologies provide the modeling backbone and are even found in unexpected places Similarity measures for ontologies exist and give back some results Criteria for semantic similarity measures are still in the making There is a lack of theory for ontology-based similarity There is a lack of efficient realization of ontologybased similarity Targeted Side Effect: Clarification of Some Often Mistaken Use of Terminology around Ontologies 44

45 Institut WeST Web Science & Technologies Thank You! Semantic Web Web Retrieval Interactive Web Multimedia Web Software Web egovernment emedia escience eorganizations ecitizen 45