- From: Butler, Mark <Mark_Butler@hplb.hpl.hp.com>
- Date: Mon, 24 Mar 2003 12:42:50 -0000
- To: "'simile@cally.hpl.hp.com'" <simile@cally.hpl.hp.com>, "'www-rdf-dspace@w3.org'" <www-rdf-dspace@w3.org>
Apologies in advance as this is skipping on from the use cases, but it seems to me one of the key questions in SIMILE (and the semantic web) is whether it is possible to interoperate between multiple vocabularies. Interestingly quite a bit of work has been done on this before - as I have mentioned the KRAFT project between BT PLC and Liverpool, Aberdeen and Cardiff Universities considered this problem. http://www.csd.abdn.ac.uk/~apreece/Research/KRAFT.html KRAFT stands for Knowledge Re-use and Fusion / Transformation This section from "Ontological Structures for Knowledge Sharing" M J R Shave, www.csc.liv.ac.uk/~mshave/NRIN+.ps <http://www.csc.liv.ac.uk/~mshave/NRIN+.ps seems particularly relevant: "The specification of mapping functions is clearly a key task in the construction of a KRAFT network. Knowledge sources can differ in their content (their data and its structure), their paradigm (the modelling convention used, such as an object oriented database or a knowledge base), their representational language (such as predicate calculus, or frames), and their ontology. The resolution of each of these differences presents problems, but ontological mapping is in many ways the hardest task, if only because it is the least well understood. Considerable attention has been devoted in the KRAFT project to identifying and classifying the ways in which differences between ontologies can occur, and how such mismatches can be resolved. In the simplest cases differences can be caused by synonyms (`client' and `customer' often have the same meaning), by homonyms (a `crest' can be a badge, or the top of a wave or hill), or by differences in underlying assumptions (`pass' and `fail' are familiar concepts but have widely varying interpretations). Other mismatches are more complex and less straightforward to resolve. Two broad categories of mismatch have been recognised. Conceptualisation mismatches result from differences in the categories or data structures used. Explication mismatches result from the definitions used, which may differ in their terminology, their formulae, or the concepts which they are defining. Some examples are: Ontology 1 uses the classes mammals, birds Ontology 2 uses the classes carnivores, herbivores A class conceptualisation mismatch Ontology 1 uses the relation hascomponent Ontology 2 uses the relation ismadeof [eg The pair {house, brick} meets either ontology, but {house,roof} fits only the first] A relation conceptualisation mismatch Ontology 1 : ship (X) < seagoing (X) ^ large (X) Ontology 2 : vessel (X) < seagoing (X) ^ large (X) A term explication mismatch Ontology 1 : ship (X) < seagoing (X) ^ large (X) Ontology 2 : vessel (X) < floating (X) ^ big (X) A term and formula explication mismatch Ontology 1 : ship (X) < seagoing (X) ^ large (X) Ontology 2 : whale (X) < seagoing (X) ^ large (X) A concept and term explication mismatch A detailed discussion of these issues can be found in [8]. [8] P R S Visser, D M Jones, T J M Bench-Capon and M J R Shave, "An Analysis of Ontology Mistmatches: Heterogeneity versus Interoperability", AAAI Spring Symposium of Ontological Engineering, California 1997 br Mark H. Butler, PhD Research Scientist HP Labs Bristol mark-h_butler@hp.com Internet: http://www-uk.hpl.hp.com/people/marbut/
Received on Monday, 24 March 2003 07:43:09 UTC