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Figure 8.10 displays mappings from an ontology to an electronic commerce taxonomy (for example, a portion of the UNSPSC product and service taxonomy). On the right in the figure is the reference ontology with its semantically well-defined relationships; on the left is the taxonomy to be used for an electronic commerce application with its less well-defined relationships. In practice, you may need to maintain mappings between ontologies (or, as in this example, between ontologies and taxonomies) simply because each knowledge representation system may be managed by separate organizations and need to evolve separately. In general, determining the semantic equivalence (mappings) between concepts in two ontologies is hard and requires human knowledge of the semantics of the two sides and thus human decision making (though current ontology management tools do have some automated support) to make the correct mappings. Although the names (labels) of two concepts may be the same (or completely different) in the two ontologies, there is no guarantee that those concepts mean the same thing (or mean different things). We've seen earlier that terms (words or labels) have very weak semantics in themselves; string identity cannot be relied on to provide semantic identity or equivalence. Similarly, structural correspondence cannot be relied on to ensure semantic correspondence. Determining semantic equivalence and then creating mappings between two ontologies will remain only a semi-automated process for quite some time in the future.
Simple, Informal E-commerce Application Taxonomy
^•Well-defined subclass relation — Other ontological relations
Ill-defined parent-child relation
Figure 8.10 Mapping ontologies.
Most ontology languages and their supporting tools have some facility for defining mappings between ontologies. The simplest mechanism is an include or import statement, whereby one ontology includes or imports another ontology. This is the simplest mechanism, because you just bring in the entire ontology into your current ontology and all the concepts and relations of the imported ontology are available to the new expanded ontology space. However, these new imported concepts and relations are not really semantically rectified (made semantically meaningful with respect to the preexisting ontology which included them).14 After importing, a tighter semantic rectification can be undergone, by merging or mapping the old and new concepts and relations. Merging will result in consolidated concepts and relations; mapping will keep the concepts and relations from both ontologies distinct but linked. Tools such as Protégé and Ontolingua/Chimaera support the merging process better than they support the mapping process. Semantic Web languages such as DAML+OIL and OWL have constructs that enable you to import an ontology, but also to declare that two concepts (represented as classes) are the same semantically. In addition, OWL enables you to declare that two individuals (instances) are the same semantically—a good step toward greater support for semantic mapping.
14The OWL:imports statement actually does semantically rectify insofar as it both includes an ontology and declares that the assertions defining the meaning of the included ontology are to be included too. This imports is stronger than most includes and may in fact fail because of semantic disagreement between the two ontologies.
Now that we have seen the primary representation levels needed for expressing ontologies and some of the issues involved in mapping between ontologies, let's look in more detail at knowledge representation languages, formalisms, and logics—the highest level of representation.
Languages, Formalisms, Logics_________________________________________
This section introduces some background material on knowledge representation, a technical discipline from the field of artificial intelligence, to assist in our understanding of ontologies and the ontology languages of the Semantic Web. We will try to keep our discussion brief, but yet provide enough detail so that technical managers and leads have some scaffolding on which to support the ontological notions we have presented in this chapter.
A wealth of material is already available on knowledge representation. This section provides some references for those who are interested in obtaining more detail, but we will focus on just a few questions: What is knowledge representation, what are the important principles and components of knowledge representation, and how is knowledge representation related to ontologies and the Semantic Web?
Semantic Networks, Frame-Based KR, and Description Logics
Knowledge representation is a branch of artificial intelligence that focuses on the design and implementation of languages and systems that represent knowledge about the world. A knowledge representation therefore is a standin for real objects in the world, and the events and relationships those real things participate in. In our earlier discussion about representation, we saw that one of the important issues is that representation is a means for both expressing and using information. So knowledge representation is a means for both expressing and using semantic information, that is, knowledge about the world or specific domains of the world, with the additional qualification that the use of that knowledge should be used for intelligent reasoning and be computationally efficient.15