1. By Helen Howell

2. 2 Outline ] What is interoperation? ] Why? ] Problems ] Approach to Interoperations ] ONION ] Cyc

3. 3 What is Interoperation? Interoperation may be defined as a loose coupling across information sources, semantic metadata descriptions and ontologies

4. 4 Why? ] Share information

5. 5 Domain Differences ] Naming attribute items differently ] payroll - EMP; personnel - PEOPLE ] Scope ] payroll may include suport for student benefits for employee’s children; personnel - employees only

6. 6 Domain Differences (cont.) ] Encoding ] ssn with and without hypens ] Attribute scopes-Semantic hetergeneity ] hot has different meaning in weather domain than in truck-engine domain

7. 7 Integration Problems ] Data that is available is subject to implicit assumptions which means that it cannot be reused out of context. ] Data is often duplicated and labeled under different terms. e.g., Address verses Addr

8. 8 Approaches ] Direct Translation ] Single Shared Ontology ] Multiple Shared Ontology

9. 9 Direct Translation For each ontology a set of translating functions is provided to allow mappings with the other ontologies.

10. 10 Direct Translation ] Only feasible if a few ontologies ] OBSERVER - mappings between a term in a source ontology and its synonym in a destination ontology. ] Set up relationships e.g., Volcano effects Environment

11. 11 Single Shared Ontology Often standards are not convenient to use since they have to be suitable for all potential uses. So a single subset is formed as a standard

12. 12 Multiple Shared Ontologies Locates shared knowledge in multiple but smaller shared ontologies

13. 13 Multiple Shared Ontologies ] Referred to as Resource Clustering or Ontology Clustering ] Approach more flexible and scalable ] No longer have to commit to a comprehensive ontology

14. 14 Multiple Ontologies ] Normally organized into a hierarchy ] Top level provides general definitions share by all resources ] Lower levels provide definitions of concepts that extend and characterize upper levels

15. 15 Mediation-Based Information Systems ] Mediators provide intelligent middleware services - matching resources to applications - resources are autonomous and heterogeneous

16. 16 Mediation Based Information Systems ] Identify most relevant resources for user’s needs ] Retrieve the relevant data ] Translate it to a common representation e.g., Nevada, NV, NV USA

17. Interoperation of Information Sources via Articulation of Ontologies Prasenjit Mitra, Gio Wiederhold Stanford University Supported by AFOSR- New World Vistas Program

18. 18 Interoperation of Information Sources ] Compose information ] Multiple independent, heterogeneous sources ] Reliability, scalability ] Semantic heterogeneity - same term different semantics - different term same semantics

19. 19 Onion System

20. 20 Articulation Generator Driver Ont1Ont2 Phrase Relator Thesaurus Structural Matcher Semantic Network Human Expert Context-based Word Relator OntA

21. 21 Preliminaries: Ontology ] Ontology - hierarchy of terms and specification of their properties. ] Modeled as a directed labeled graph + set of rules. ] Ont = ( V, E, R) V - set of nodes(concepts) E - set of edges(properties) R - set of rules involving V,E

22. 22 Articulation Rules ] Articulation rules - logic-based rules that relate concepts in two ontologies: -Binary Relationships (O1.Car SubClassOf O2.Vehicle) (O1.Buyer Equ O2.Owner) (O2.LuxuryCar SubClassOf O1.Car)

23. 23 Example Car H123 Instance List_Price VID PMitra Name Unit $ 40kCA Address Amount Vehicle LuxuryCar PMitra Buyer Instance SubClass Instance VIN H123 OwnerRetail_ Price CA NameAddr $35k SubClass Buyer Owner Attribute Equ

24. 24 Articulation Rules (contd.) ] Horn Clauses - (O1.Car O1.Instance X), (X O1.Price Y), (Y > $30000) => (O2.LuxuryCar Instance X) - (V O2.Retail_Price P), (C O1.List_Price L), (L Unit U), (L Amount A), (V Equ C) => (P Equ concat(U,A))

25. 25 Contributions ] Articulation Generation Toolkit - produce translation rules semi-automatically - a library of reusable heuristic methods - a GUI to display ontologies and interact with the expert ] Ontology Algebra - query rewriting and planning

26. 26 Articulation Generation Methods ] Non-iterative Methods - Lexical Matcher - Thesaurus-based Matcher - Corpus-based Matcher - Instance-based Matcher ] Iterative Methods - Structural Matcher - Inference-based Matcher

27. 27 Lexical Methods ] Preprocessing rules. -Expert-generated seed rules. e.g., (O1.List_Price Equ O2.Retail_Price) -Context-based preprocessing directives. e.g., (O1.UK_Govt Equ O2.US_Govt) -Stop-word Removal & Stemming -Word match (full or partial) -Phrase match e.g., (O1.Ministry_Of_Defence Equ O2.Defense_Ministry) 0.6

28. 28 Thesaurus-based methods ] Consult a dictionary/thesaurus to find synonyms, related words ] Generate a similarity measure or relatedness measure - words that have similar words in their definitions are similar ] Get more semantically meaningful relationships from WordNet (syn, hyper)

29. 29 Candidate Match Repository Term linkages automatically extracted from 1912 Webster’s dictionary * * free, other sources. being processed. Based on processing headwords ý definitions Notice presence of 2 domains: chemistry, transport

30. 30 Corpus-based ] Collect a set of text documents preferably from same domain - search using keywords in google ] Build a context vector (1000-character neighbourhood) for each word ] Compute word-pair similarity based on the cosine of the vectors ] Use word-pair similarity to find similarity among labels of nodes/edges

31. 31 Structural Methods ] Uses results of lexical match ] If x% of parent nodes match & y% of children nodes match ] Special relations (AttributeOf) match

32. 32 Tools to create articulations Graph matcher for Articulation- creating Expert Vehicle ontology Transport ontology Suggestions for articulations

33. 33 continue from initial point Also suggest similar terms for further articulation: by spelling similarity, by graph position by term match repository Expert response: 1. Okay 2. False 3. Irrelevant to this articulation All results are recorded Okay ’s are converted into articulation rules

34. 34 An Ontology Algebra Operations can be composed Operations can be rearranged Alternate arrangements can be evaluated Optimization is enabled The record of past operations can be kept and reused when sources change

35. 35 Binary Operators A knowledge-based algebra for ontologies The Articulation Function (ArtGen), given two ontologies, supplies articulation rules between them. Intersection create a subset ontology keep sharable entries Union create a joint ontology merge entries Difference create a distinct ontology remove shared entries

36. 36 Intersection: Definition ] O1 = (V1, E1, R1) O2 = (V2, E2, R2) ] OI = ( O1 Int ArtGen O2) = (VI, EI, RI) ] Arules = ArtGen( O1, O2 ) ] VI = Nodes( Arules ) ] EI = Edges( Arules ) + Edges(E1, VI.V1) + Edges( E2, VI.V2) ] RI = Arules + Rules( R1, VI.V1) + Rules( R2, VI.V2)

37. 37 Intersection: Example ] ARules = { (O1.Car SubClass O2.LuxuryCar), (O1.MSRP Equ O2.Price)} ] NI = ( O1.Car, O1.MSRP, O2.LuxuryCar, O2.Price ) ] EI = Edges(ARules) + {(O1.Car Attribute O1.MSRP), (O2.LuxuryCar Attribute Price)} InexpCar Car LuxuryCar MSRPPrice LuxuryTax SubClass Equ O1 O2 OI

38. 38 Intersection: Properties ] Commutative? OI 12 =(O1 Int ArtGen O2) = (O2 Int ArtGen O1)=OI 21 VI 12 = VI 21, EI 12 = EI 21, RI 12 =RI 21 ARules 12 = ARules 21 ArtGen(O1, O2) = ArtGen(O2, O1) ] Int ArtGen is commutative iff ArtGen is commutative

39. 39 Semantically Commutative Example: ] ArtGen(O1,O2) : ( O1.Car SubClassOf O2.Vehicle) ] ArtGen(O2,O1) : ( O2.Vehicle SuperClassOf O1.Car) ] Defn: ArtGen is Semantically Commutative iff ArtGen(O1,O2) ArtGen(O2,O1) Operands to intersection can be rearranged if ArtGen is semantically commutative

40. 40 Associativity Example ] Example: ] ArtRules(O1, O3) : (O1.Car SubClassOf O3.Vehicle) ] ArtRules(O2, O3) : (O2.Truck SubClassOf O3.Vehicle) ] ArtRules(O1, O2) : null ] Intersection is not associative!

41. 41 Associativity ] (O1.O2).O3 = O1.(O2.O3) iff ArtGen is consistent and transitively connective ] consistent - given two nodes it generates the same relationship irrespective of relationships between other nodes

42. 42 Associativity (contd.) ] ArtGen relates A and B: (A Rel B) = (A R1 B) or (B R2 A) ] Transitively connective: If ArtGen generates (A Rel B), (B Rel C) then it also generates (A Rel C’)  where A  O1, B  O2, C,C’  O3

43. 43 Using the Articulation Rules ] Q(X) :- (LuxuryCar Instance X), (X Owner Y), (Y Addr `CA’) ] Translated using Articulation Rules Q(X) :- (Car Instance X),(X List_Price Y) (Y Unit ‘$’),(Y Amount A), (A > 30000), (X Buyer Z), (Z Address ‘CA’)

44. 44 Conclusion ] Introduced an Ontology Interoperation System. ] Interoperation based on articulations that bridge the semantic gap. ] Graph-oriented data model, logical rules ] Founded on Ontology Algebra Thanks to Stefan Decker, Alex Carobus, Jan Jannink, Sergey Melnik, Shrish Agarwal

45. DAML Ontology Interoperation Using Cyc Steve Reed Cycorp

46. 46 DAML Excitement at Cycorp Promoting DAML ontology interoperability through Cyc, as a reference ontology. Cyc now imports and exports DAML, paraphrases and reasons about it. Cyc lexical tools assist the mapping of imported DAML ontologies into Cyc. Both UDDI categorization schemes imported as DAML into Cyc: –UNSPSC (via KSL) and NAICS Cycorp is featuring DAML in it’s upcoming release of OpenCyc (this fall).

47. 47 DAML Challenges at Cycorp How to automate mapping and translation of DAML ontologies. How to get the user community to help map numerous DAML terms -- interest growing at OpenCyc. Importation of large DAML schema is daunting but underway, WordNet, OpenDirectory, UNSPSC, NAICS Provide an inference engine for the Semantic Web - subsumption, classification, constraints, well-formed statement validation, diagnostics, justifications. What web services interface? SOAP likely.

48. DAML Ontology Interoperation Cyc Ontology UNSPSC Ontology NAICS Ontology Mapping 1: UNSPSC to Cyc Mapping 2: NAICS to Cyc User Search for: “waterproof plywood” Search for: UNSPSC/7311 Hardwood Int. … marine plywood … UDDI Web Services Return: marine plywood by Hardwood Int.

49. 49 Ontology Mapping From Doerr, Semantic Problems of Thesaurus Mapping DAML Import

50. 50 Ontology Mapping From Doerr, Semantic Problems of Thesaurus Mapping DAML Import Cyc reference

51. 51 UNSPC/NAICS Mapping Universal Standard Products and Services Classification, North American Industrial Classification System Both business taxonomies are monohierarchies, entities should have one code assigned for them. Cyc: #$MonohierarchyClassificationSystem implies that each collection is #$partitionedInto its narrower terms. Mapping is targeted at facets of the reference Cyc terms via functional composition.

52. 52 Mapping to Semantic Facets (#$isa (#$BusinessFacetFn #$HardwoodInternational #$Hardware) #$NAICS-Hardware-Store)) Hardwood International is an online store, selling mainly lumber but offering hardware as well, but NAICS is a monohierarchy. Hardwood International 44413 Hardware (#$isa (#$BusinessFacetFn #$HardwoodInternational #$Hardwood) #$NAICS-Other-Building-Materials-Dealers)) 44419 Hardwood Business facets

53. 53 Conclusion ] Ontology Interoperation is not without it problems ] Hetergeneous data can work together

54. By Helen Howell