prompt: algorithm and tool for automated ontology merging and alignment natalya fridman noy and mark...

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

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  • Slide 1
  • PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment Natalya Fridman Noy and Mark A. Musen
  • Slide 2
  • Motivation Overview of PROMPT Related Work Knowledge Model PROMPT Algorithm Prot g -based PROMPT Tool Evaluation Discussion Conclusions
  • Slide 3
  • 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.
  • Slide 4
  • 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 solutions Based on the Prot g -2000 knowledge-modeling environment Can be applied across various platforms
  • Slide 5
  • Related Work Ontology design Object-oriented programming Heterogeneous databases
  • Slide 6
  • Related Work -Ontology Design and Integration Chapulsky et al. 1997, Scalable Knowledge Composition project Ontomorph Chimaera based on Ontolingua ontology editor Medical vocabularies
  • Slide 7
  • 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
  • Slide 8
  • 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
  • Slide 9
  • Knowledge Model Classes Slots Facets Instances
  • Slide 10
  • PROMPT Algorithm Input: two ontologies Output: one merged ontolgy
  • Slide 11
  • Gist of PROMPT Identify a set of knowledge-base operations for ontology merging or alignment For 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
  • Slide 12
  • Ontology-merging Operations and Conflicts
  • Slide 13
  • 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 linguistically similar names Check for redundancy in the parent hierarchy for M
  • Slide 14
  • Prot g -based PROMPT Tool Setting the preferred ontology Maintaining the user s focus Providing feedback to the user Logging and reapplying the operations
  • Slide 15
  • Evaluation Using Prot g -2000 with PROMPT Using generic Prot g -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
  • Slide 16
  • 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%
  • Slide 17
  • PROMPT versus Generic Prot g PROMPTGeneric Prot g 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 6016
  • Slide 18
  • PROMPT versus Chimaera PROMT Chimaera Correct suggestions 20%
  • Slide 19
  • Discussion The choice of source ontologies Differences between PROMPT and Chimaera
  • Slide 20
  • 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, needs more test Users may be overwhelmed by too many specific suggestions
  • Slide 21