3rd message from Protege list.
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_____________________________________________
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
Forwarded message 1
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
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