prompt: algorithm and tool for automated ontology merging and alignment

PROMPT:Algorithm and Tool for Automated Ontology Merging and Alignment

Natalya F. Noy

Stanford Medical Informatics

Stanford University

Outline

Definitions and motivation The PROMPT ontology-merging algorithm

Incremental algorithm (PROMPT) Statistical algorithm (Anchor-PROMPT)

The tools Evaluation Future work

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

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

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

What Is Ontology Merging

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

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 approachesdo not provide any guidance to the user,

do not use structural information

Outline




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

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)

Make initial suggestions

Select the next operation

Perform automatic updates

Find conflicts

Make suggestions

The PROMPT Algorithm

Example: merge-classes

Agencyemployee

Agent

Customer

subclass of

agent for

Agent

Employee

Traveler

subclass of

has client

Agencyemployee

Agent

Employee

Customer Traveler

subclass of subclass of

agent for has client

Example: merge-classes (II)

Agencyemployee

Agent

Employee

Customer Traveler


agent for has client

Agencyemployee

Agent

Employee

Customer Traveler


agent for

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

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 reinforce or downgrade existing suggestions

Conflicts

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

inheritance

Agent Agent

Agent

Example: merge-classes

Operation Steps: merge-classes

Own slot and their values for the new classask the user in case of conflicts or use preferences

Template slots for the new classunion of template slots of the original classes

Subclasses and superclasses for the new class

Conflicts Suggestions

Agent Agent

Agent

agent for

Template Slots

Copy template slots that don’t exist in the merged ontology

agent for

Agent Agent

Agent

has client

clientclient

Template Slots

Attach the slots that have already been mapped

Employee

Subclasses And Superclasses

If a superclass (subclass) exists, re-establish the links

Agent Agent

Agent

Agencyemployee

superclass

superclass

Agent

Dangling References

Agent Agentagent for

Customer

facet value

For example,allowed class

agent forfacet value

Customer _temp

dummy frame

Agentclient

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

hasclients

handlesreservations

Agency employee

Employee Agencyemployee

Agent

If names of superclasses (subclasses) of the merged class are similar, suggest to merge the classes

superclasssuperclass

Additional Suggestions: Merge Classes

Check for Cycles

Person

Employee Agencyemployee

Agent

superclass

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

Non-local context

Classes directly referenced by C

Slots in C

Context

C

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 1 Ontology 2

Generating Paths in the Graph

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

Equivalence Groups

Anchor-PROMPT: Initial Results

TRIAL Trial

PERSON Person

CROSSOVER Crossover

PROTOCOL Design

TRIAL-SUBJECT Person

INVESTIGATORS Person

POPULATION Action_Spec

PERSON Character

TREATMENT-POPULATION Crossover_arm

Knowledge Model Assumptions

The only assumption:

An OKBC-compliant knowledge model

Outline




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-200 plugins

Domain-specific user-interface plugins Alternative back ends for archival storage Utility programs for knowledge-acquisition

tasks End-user applications

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




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

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)?

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

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 operationsgenerated 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

0

20

40

60

PROMPT Protégé

1660

Results

Experts followed most of the PROMPT’s suggestions

Using PROMPT has improved the efficiency of ontology merging

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

Anchor-PROMPT: Evaluation Results

Max path length

Number of anchors

Result precision

4 4 67%4 3 67%4 2 61%3 4 67%3 3 61%3 2 56%2 4 100%2 3 100%

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

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

http://protege.stanford.edu

prompt: algorithm and tool for automated ontology merging and alignment

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