prompt: algorithm and tool for automated ontology merging and alignment natalya fridman noy and mark...
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PROMPT: Algorithm and Tool for Automated Ontology Merging and
Alignment
Natalya Fridman Noy and Mark A. Musen
Motivation Overview of PROMPT Related Work Knowledge Model PROMPT Algorithm Protégé-based PROMPT Tool Evaluation Discussion Conclusions
Motivation
A variety of ontologies exist in many domain areas, for the purpose of ontology reuses, merging or alignment is necessary
– Merging: create a single coherent ontology that includes the information from all the sources
– Alignment: make the sources consistent and coherent with one another but kept separately
Manually to merge or align is a laborious and tedious work (DARPA’s High performance Knowledge-Bases project)
Many steps in the process of merging or alignment is possible to be automated.
Overview of PROMPT
A Formalism-independent algorithm for ontology merging and alignment
Automate the process as much as possible Guide the users when it is necessary Suggest possible actions Determine conflicts in the ontologies and propose soluti
ons Based on the Protégé-2000 knowledge-modeling envir
onment Can be applied across various platforms
Related Work
Ontology design Object-oriented programming Heterogeneous databases
Related Work-Ontology Design and Integration
Chapulsky et al. 1997, Scalable Knowledge Composition project
Ontomorph Chimaera based on Ontolingua ontology editor Medical vocabularies
Related Work-Object-Oriented Programming
Subject-Oriented programming (SOP)– Subjects: collections of classes that represent subjective views
of, possibly, the same universe that need to be combined
Relies more heavily on the operational methods associated with classes rather than on declarative relations among classes and slots
Alignment is uncommon in composition of object-oriented hierarchies
Related Work-Heterogeneous Databases
The common theme in the research on heterogeneous databases: bridge the gaps on demand by creating an extra mediation layer– Develop mediators– Define a common data model– Specify a set of matching rules
Usually integrated at the syntactic rather than semantic level
Knowledge Model
Classes Slots Facets Instances
PROMPT Algorithm
Input: two ontologies
Output: one merged ontolgy
Gist of PROMPT
Identify a set of knowledge-base operations for ontology merging or alignmentFor each operation, define:
1. Changes that PROMPT performs automatically
2. New suggestions that PROMPT presents to the user
3. Conflicts that operation may introduce and that the user needs to resolve
Ontology-merging Operations and Conflicts
Example (for merging classes A and B to M)
For each slot S that was attached to A and B in the original ontologies
For each superclass of A and B that has been previously copied into the merged ontology
For each class C in the original ontologies to which A and B preferred
For each class C that was a facet value for A or B and that has not been copied to the merged ontology
For each pair of slots for M that have linguistically similar names For each pair of superclasses and subclasses of M that have lingu
istically similar names Check for redundancy in the parent hierarchy for M
Protégé-based PROMPT Tool
Setting the preferred ontology Maintaining the user’s focus Providing feedback to the user Logging and reapplying the operations
Evaluation
Using Protégé-2000 with PROMPT Using generic Protégé-2000 Using Chimaera
Input: two ontologies :A and B contained totally of 134 class and Slot frames.
A: the ontology for the unified problem-solving method Development language
B: the ontology for the method description language
Quality of PROMPT’S Suggestions
% of suggestions that human experts followed
% of conflict-resolution strategies that human experts followed
% of total knowledge-base operation suggestions
90% 75% 74%
PROMPT versus Generic Protégé
PROMPT Generic Protégé
Contents of the resulting merged ontologies
Can find all the classes that should have been merged
Some minor differences in class hierarchy, slot names and types
Number of explicit KB operations that user has to specify
60 16
PROMPT versus Chimaera
PROMT
Chimaera
Correct suggestions
20%
Discussion
The choice of source ontologies Differences between PROMPT and Chimaera
Conclusions
Be able to perform a large number of merging operations
The quality of result should be evaluated The result for larger ontologies is unknown, ne
eds more test Users may be overwhelmed by too many speci
fic suggestions