3rd message from Protege list. -- _____________________________________________ Dr. Leo Obrst The MITRE Corporation mailto:lobrst@mitre.org Intelligent Information Management/Exploitation Voice: 703-883-6770 7515 Colshire Drive, M/S W640 Fax: 703-883-1379 McLean, VA 22102-7508, USA
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Hi, As people on this list have pointed out, you won't find many algorithms for mapping between ontologies completely automatically. And, in any case, you need to be aware that all of those algorithms are (collections of) heuristics. Don't get me wrong -- there is nothing wrong with the heuristic-based approach. In fact, I personally believe that this approach is the only possible one in the case of ontology mapping: So much of the semantics is always implicit (even in ontologies), that it is almost impossible to have any formal definition of what it means for a concept in one ontology to map into concept in another ontology. If you want this process to be precise, you always need some form of human involvement, if only to verify the mappings with which the automatic algorithm came up with. That being said, recently there has been a number of approaches for finding candidates for mapping automatically, both in the ontology-mapping/merging field and in the schema matching field. In case you haven't got enough references already, here are some more for your list: The ONION project in the Stanford DB group, in particular http://www-db.stanford.edu/SKC/publications/match.ps They use some seed matching rules from the user to generate new matching rules (and have the user prune them). FCA-merge from University of Karlsruhe: http://www.fzi.de/wim/publications/2001/federated_ijcai_ws.pdf and their IJCAI-2001 paper. They use a set of instances that are common to the two ontologies to generate some possible matches (assuming these common instances exist). The LSD project at University of Washington (http://www.cs.washington.edu/homes/anhai/lsd/lsd.html) uses machine-learning techniques to use XML data to produce matches. Our Anchor-PROMPT algorithm (http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-2001-0889.html) is another one that attempts to produce some candidate matches between ontologies for user's perusal. For a review of automatic and semi-automatic mapping for database schemas, check out the following very comprehensive review: http://research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2001-17 For a couple of recent (not included in that review) semi-automatic approaches to schema matching see the similarity-flooding algorithm (http://dbpubs.stanford.edu:8090/pub/2001-25) and the Cupid project (http://research.microsoft.com/~philbe/CupidVLDB01.pdf) Enjoy, Natasha ---------------------------------------------------------------------- To unsubscribe, send email to majordomo@smi.stanford.edu with "unsubscribe protege-discussion" in the message body (no quotes). If this doesn't work, contact owner-protege-discussion@smi.stanford.eduReceived on Friday, 18 January 2002 18:46:13 GMT
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