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

2. Problem: Using Multiple Ontologies that do not Conform to one another Whether Merging/Alignment, user goes through same steps Whether Merging/Alignment, user goes through same steps Establishes correspondences between sources in the following way by hand: Establishes correspondences between sources in the following way by hand: –Set of overlapping concepts –Set of concepts similar in meaning but different names/structure –Set of concepts unique to each source Authors did this, thought it was tedious, saw opportunities for a tool to semi-automate the process Authors did this, thought it was tedious, saw opportunities for a tool to semi-automate the process

3. PROMPT Automates process as much as possible Automates process as much as possible When not possible, PROMPT guides user to places where intervention is necessary, suggests possible actions, determines conflicts and proposes solutions When not possible, PROMPT guides user to places where intervention is necessary, suggests possible actions, determines conflicts and proposes solutions

4. Related Work Most prior work done on syntactic matches Most prior work done on syntactic matches Ontomorph Ontomorph –Guidance only at initial list of matches Chimaera Chimaera –Suggestions just point to where something needs to change, doesn’t suggest what to do

5. Prompt’s Knowledge Model Designed to be compatible with OKBC Designed to be compatible with OKBC Classes Classes –Subclass/Superclass multiple inheritance – each instance has slots – slots are inherited by subclasses Slots Slots –Named binary relations between classes or a class and a primitive – can be constrained by facets Facets Facets –Named ternary relations between a class, slot, and another class or primitive – can constrain slots with cardinality or value type Instances Instances –Members of a class

6. The Algorithm

7. Operations Operations –Merge classes –Merge slots –Merge bindings between slot/class –Perform a deep copy of a class –Perform a shallow copy of a class Conflicts Conflicts –Name conflicts –Dangling references –Redundancy in class hierarchy –Slot-value restrictions that violate class inheritance

8. Protégé-based PROMPT Setting the preferred ontology Setting the preferred ontology Maintaining the user’s focus Maintaining the user’s focus Providing feedback to the user Providing feedback to the user –Why PROMPT made a suggestion Logging and reapplying the operations Logging and reapplying the operations –Ontologies keep updating, still, merging process should be easier

9. Evaluation Same source O’s for each evaluation, all testers experts of those O’s, unfamiliar with PROMPT, unlimited time to complete (change that next time) Same source O’s for each evaluation, all testers experts of those O’s, unfamiliar with PROMPT, unlimited time to complete (change that next time) Measured quality of PROMPT’s suggestions by Measured quality of PROMPT’s suggestions by –How many of PROMPT’s suggests the expert followed = 90% –How many of the conflict solutions the expert followed = 75% –PROMPT suggested 74% of total operations invoked in merging procedure

10. Evaluation Utility: Protégé-based PROMPT or just Protégé- 2000 Utility: Protégé-based PROMPT or just Protégé- 2000 –The two merged ontologies were similar, 1 diff in class hierarchy, various diffs in slot names and types –Generic Protégé-2000 explicitly specified 60 knowledge- base operations –PROMPT user explicitly specified only 16 operations

11. Evaluation PROMPT vs Chimaera PROMPT vs Chimaera –Executed the same sequence of merging steps, and compared the set of new suggestions, used one of the testers to previously merged the ontologies to judge whether the suggestions from each system were correct –PROMPT had 30% more correct suggestions than Chimaera –Chimaera’s suggestions was a proper set of PROMPT’s –Of Chimaera’s correct suggestions, 20% were the same in PROMPT, 80% were more specific in PROMPT

12. Discussion The actions that PROMPT performs on its own saves the expert time and effort The actions that PROMPT performs on its own saves the expert time and effort The source ontologies were small/uncontroversial, not indicative of reality, did not allow for comparing quality of results The source ontologies were small/uncontroversial, not indicative of reality, did not allow for comparing quality of results PROMPT gives a more specific list of suggestions than Chimaera PROMPT gives a more specific list of suggestions than Chimaera Authors plan to define more heuristics for more automation, extend approach to OKBC facets, include class instances, and axioms for constraint on frames in the ontology Authors plan to define more heuristics for more automation, extend approach to OKBC facets, include class instances, and axioms for constraint on frames in the ontology