1. PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment Natalya F. Noy Stanford Medical Informatics Stanford University

2. Outline Definitions and motivation The PROMPT ontology-merging algorithm Incremental algorithm (PROMPT) Statistical algorithm (Anchor-PROMPT) The tools Evaluation Future work

3. Ontologies Characterize concepts and relationships in an application area, providing a domain of discourse Enumerate concepts, attributes of concepts, and relationships among concepts Define constraints on relationships among concepts

4. Why do we need ontologies An ontology provides a shared vocabulary for different applications in a domain An ontology enables interoperation among applications using disparate data sources from the same domain

5. Ontologies Are Everywhere Ontologies have been used in academic projects for a long time Knowledge sharing and reuse Reuse of problem-solving methods Ontologies are becoming widely used outside of academia Categorization of Web sites (e.g. Yahoo!) Product catalogs

6. Need for Ontology Merging There is significant overlap in existing ontologies Yahoo! and DMOZ Open Directory Product catalogs for similar domains

7. Need for Ontology Merging and Integration Need to merge or align overlapping ontologies Chemdex™—a portal for accessing life- science–supply catalogs Workshop on “Ontologies and Information Sharing” at IJCAI’2001 6 out of 18 papers (1/3) are about ontology merging and integration

8. What Is Ontology Merging

9. Existing Approaches Ontology design and integration term matching (Stanford SKC, ISI) graph-based analysis (Stanford SKC) transformation operators (Ontomorph at ISI) merging tools (Chimaera at Stanford KSL) Object-oriented Programming subject-oriented programming (IBM) “subjective” views of classes transformation operations concentrates on methods rather than relations

10. Existing Approaches (II) Databases develop mediators and provide wrappers define a common data model and mappings define matching rules to translate directly Most of these approaches do not provide any guidance to the user, do not use structural information

11. Outline Definitions and motivation The PROMPT ontology-merging algorithm Incremental algorithm (PROMPT) Statistical algorithm (Anchor-PROMPT) The tools Evaluation Future work

12. PROMPT Our approach is: Partial automation Algorithms based on concept-representation structure relations between concepts user’s actions Our approach is not: Complete automation Algorithm for matching concept names

13. Knowledge Model A generic knowledge model of OKBC (Open Knowledge- Base Connectivity Protocol) Classes Collections of objects with similar properties Arranged in a subclass–superclass hierarchy Instances Slots First-class objects in a knowledge base Binary relations describing properties of classes and instances Facets Constraints on slot values (cardinality, min, max)

14. Make initial suggestions Select the next operation Perform automatic updates Find conflicts Make suggestions The PROMPT Algorithm

15. Example: merge-classes Agency employee Agent Customer subclass of agent for Agent Employee Traveler subclass of has client Agency employee Agent Employee Customer Traveler subclass of agent for has client

16. Example: merge-classes (II) Agency employee Agent Employee Customer Traveler subclass of agent for has client Agency employee Agent Employee Customer Traveler subclass of agent for

17. Analyzing Global Properties Locally Global properties classes that have the same sets of slots classes that refer to the same set of classes slots that are attached to the same classes Local context incremental analysis consider only the concepts that were affected by the last operation

18. The PROMPT Operation Set Extends the OKBC operation set with ontology- merging operations merge classes merge slots merge instances copy of a class deep or shallow with or without subclasses with or without instances …

19. After a User Performs an Operation For each operation perform the operation consider possible conflicts identify conflicts propose solutions analyze local context create new suggestions reinforce or downgrade existing suggestions

20. Conflicts Conflicts that PROMPT identifies name conflicts dangling references redundancy in a class hierarchy slot-value restrictions that violate class inheritance

21. Agent Example: merge-classes

22. Operation Steps: merge-classes Own slot and their values for the new class ask the user in case of conflicts or use preferences Template slots for the new class union of template slots of the original classes Subclasses and superclasses for the new class Conflicts Suggestions

23. Agent agent for Template Slots Copy template slots that don’t exist in the merged ontology agent for

24. Agent has client client Template Slots Attach the slots that have already been mapped

25. Employee Subclasses And Superclasses If a superclass (subclass) exists, re-establish the links Agent Agency employee superclass

26. Agent Dangling References Agent agent for Customer facet value For example, allowed class agent for facet value Customer _temp dummy frame

27. Agent client has client Additional Suggestions: Merge Slots If slot names at the merged class are similar, suggest to merge the slots

28. Agent Additional Suggestions: Merge Classes If the set of classes referenced by the merged class is the same as the set of classes referenced by another class, suggest a merge ReservationClient has clients handles reservations Agency employee

29. EmployeeAgency employee Agent If names of superclasses (subclasses) of the merged class are similar, suggest to merge the classes superclass Additional Suggestions: Merge Classes

30. Check for Cycles Person EmployeeAgency employee Agent superclass If there is a cycle, suggest removing one of the parents

31. To Summarize Perform the actual operation For the concepts (classes, slots, and instances) directly attached to the operation arguments perform global analysis for new suggestions Perform global analysis for new conflicts

32. Non-local context Classes directly referenced by C Slots in C Context C

33. Anchor-PROMPT: Using Non-Local Contexts Input: A set of anchor pairs Output: A set of related terms with similarity scores Where do anchors come from? Lexical matching Interactive tools User-specified Ontology 1Ontology 2

34. Generating Paths in the Graph

35. Similarity Score Generate a set of all paths (of length < L) Generate a set of all possible pairs of paths of equal length For each pair of paths and for each pair of nodes in the identical positions in the paths, increment the similarity score Combine the similarity score for all the paths

36. Equivalence Groups

37. Anchor-PROMPT: Initial Results TRIALTrial PERSONPerson CROSSOVERCrossover PROTOCOLDesign TRIAL-SUBJECTPerson INVESTIGATORSPerson POPULATIONAction_Spec PERSONCharacter TREATMENT-POPULATIONCrossover_arm

38. Knowledge Model Assumptions The only assumption: An OKBC-compliant knowledge model

39. Outline Definitions and motivation The PROMPT ontology-merging algorithm Incremental algorithm (PROMPT) Statistical algorithm (Anchor-PROMPT) The tools Evaluation Future work

40. Protégé-2000 An environment for Ontology development Knowledge acquisition Intuitive direct-manipulation interface Extensibility Ability to plug in new components

41. Ontologies in Protégé-2000

42. Protégé-200 plugins Domain-specific user-interface plugins Alternative back ends for archival storage Utility programs for knowledge-acquisition tasks End-user applications

43. Protégé-based PROMPT tool Protégé-2000 has an OKBC-compatible knowledge model allows building extensions through a plug-in mechanism can work as a knowledge-base server for the plug- ins

44. The PROMPT tool

46. The PROMPT tool features Setting a preferred ontology Maintaining the user’s focus Providing feedback to the user Preserving original relations subclass-superclass relations slot attachment facet values Linking to the direct-manipulation ontology editor Logging operations

47. Outline Definitions and motivation The PROMPT ontology-merging algorithm Incremental algorithm (PROMPT) Statistical algorithm (Anchor-PROMPT) The tools Evaluation Future work

48. Evaluation Knowledge-based systems are rarely evaluated We can use software-engineering approaches to empirical evaluation of tools We need to develop additional knowledge- base measurements

49. Questions we asked How good are PROMPT’s suggestions and conflict-resolution strategies? Does PROMPT provide any benefit when compared to a generic ontology-editing tool (Protégé-2000)?

50. What we were trying to find out The benefit that the tool provides Productivity benefit Quality improvement in the resulting ontologies User satisfaction Precision and recall of the tool’s suggestions

51. Source ontologies for the experiments Two ontologies of problem-solving methods the ontology for the Unified Problem-solving Method Development Language (UPML) the ontology for the Method-Description Language (MDL)

52. Experiment 1: Evaluate the quality of PROMPT’s suggestions Metrics Precision Recall Method Automatic logging Automatic data reporting Suggestions that the tool produced Operations that the user performed Suggestions that the user followed

53. Results: the quality of PROMPT’s suggestions Suggestions that users followed Conflict-resolution strategies that users followed Knowledge-base operations generated automatically 90% 75% 74%

54. Experiment 2: PROMPT versus generic Protégé-2000 Metrics content of the resulting ontologies number of explicit knowledge-base operations PROMPT

55. Results: PROMPT versus generic Protégé-2000 The resulting ontologies had only one difference Specifying operations explicitly 16 60

56. Results Experts followed most of the PROMPT’s suggestions Using PROMPT has improved the efficiency of ontology merging

57. Anchor-PROMPT Evaluation Experiment setup Two ontologies from the DAML ontology library Varying parameters maximum path length number of anchor pairs Experiment results Ratio of correct results above the median similarity score

58. Anchor-PROMPT: Evaluation Results

59. Anchor-PROMPT Evaluation Results Equivalence groups of size <= 2 are required Maximum path lengths of 2 provides extremely high precision (but low recall) 75% precision with maximum path lengths 3 and 4

60. Future work Extend the set of heuristics that PROMPT uses for guiding the experts Extend the techniques to ontology alignment and ontology refactoring Develop protocols and metrics for a more detailed evaluation of the tools

61. http://protege.stanford.edu