PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment Natalya F. Noy and Mark A. Musen

Acknowledgment Slides have been taken from www- db.stanford.edu/dbseminar/Archive/SpringY2001/ speakers/nfridmannoy I have made some modifications to them

Outline Definitions and motivation The PROMPT ontology-merging algorithm The tools Evaluation Future work

Ontologies Characterize concepts and relationships in an application area Enumerate concepts, attributes of concepts, and relationships among concepts Define constraints on relationships among concepts

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

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

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

What Is Ontology Merging

Existing Approaches Ontology design and integration term matching (Scalable Knowledge Composition) transformation operators (Ontomorph) merging tools (Chimaera) Object-oriented Programming subject-oriented programming (IBM) “subjective” views of classes transformation operations concentrates on methods rather than relations

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

Outline Definitions and motivation The PROMPT ontology-merging algorithm The tools Evaluation Future work

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

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 Binary relations describing properties of classes and instances Facets Constraints on slot values (cardinality, min, max)

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

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

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

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

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

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

Agent Example: merge-classes

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

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

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

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

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

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

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

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

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

Outline Definitions and motivation The PROMPT ontology-merging algorithm The tools Evaluation Future work

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

Ontologies in Protégé-2000

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

The PROMPT tool

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

Outline Definitions and motivation The PROMPT ontology-merging algorithm The tools Evaluation Future work

Evaluation 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)?

Performance evaluation criteria The benefit that the tool provides Productivity benefit Quality improvement in the resulting ontologies User satisfaction Precision and recall of the tool’s suggestions

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)

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

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%

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

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

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

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