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the semantic web a gide to the future of XML, Web Services and Knowledge Management - Daconta M,C.

Daconta M,C. the semantic web a gide to the future of XML, Web Services and Knowledge Management - Wiley publishing , 2003. - 304 p.
ISBN 0-471-43257-1
Download (direct link): thesemanticwebguideto2003.pdf
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The following concepts all attempt to address issues in representing, classifying, and disambiguating semantic content (meaning): taxonomies, thesauri, conceptual models, and logical theories. This section will help you distinguish these concepts. The Ontology Spectrum (see Figure 7.5) tries to depict these concepts in a general classification or ontology space, and displays the relationships among concepts such as "classification system," "taxonomy," "thesaurus," "ontology," "conceptual model," and "logical theory." The following common languages and technologies are displayed in the diagram:
■ Database models: the relational language (R), the Entity-Relational language and model (ER), and the Extended Entity-Relational model (EER)
■ Object-oriented models: Unified Modeling Language (UML)
This framework was developed for comparing the semantic richness of classification and knowledge-based models, most of which have been employed or discussed by various groups in multiple conceptual paradigms and used for the representation, classification, and disambiguation of semantics in or across particular subject matter domains. As you go up the spectrum from lower left
to upper right, the semantic richness increases. We characterize the poles of the spectrum as "weak semantics" and "strong semantics." What we mean is that the richness of the expressible or characterizable semantics increases from weak to strong. At the "weaker" side, you can express only very simple meaning; at the "stronger" side, you can express arbitrarily complex meaning.
Figure 7.5 includes terms you may not yet know much about (though we touched on a few of these in earlier chapters): DAML+OIL, OWL, description logic, first-order logic, and modal logic. Don't worry yet about what the acronyms stand for; we will describe them in detail either in this chapter or the next.
What is normally known as an ontology can thus range from the simple notion of a taxonomy (knowledge with minimal hierarchic or parent/child structure), to a thesaurus (words and synonyms), to a conceptual model (with more complex knowledge), to a logical theory (with very rich, complex, consistent, meaningful knowledge).
Strong semantics
Is disjoint subclass of with transitivity property
Weak semantics
Figure 7.5 The ontology spectrum: Weak to strong semantics.
The lower-left endpoint of Figure 7.5 designates a taxonomy. In a taxonomy, as we've seen, the semantics of the relationship between a parent and a child node is relatively underspecified or ill defined. In some cases, the relationship is the subclass of relation; in others, it is the part of relation. In still others, it is simply undefined. If you consider your computer's directory structure, the relationship between any given directory and one of its specific subdirectories is arbitrary. Say, for example that in one case, you create a subdirectory to hold a special subset (a subclass) of the directory documents; in another, you create a subdirectory to place documents representing conferences and workshops addressing the general subject matter of the parent directory.
Figure 7.6 displays a subset of a directory structure (Microsoft Windows 2000). Note that under the highlighted Logic Programming subdirectory are the logic programming systems subdirectories (Prolog and Eclipse, which might be considered subclasses of logic programming), as well as subdirectories, Logic Programming Conferences and Logic Programming Research Projects, which are just things that are related somehow (and the relationship is not really spelled out) to the parent directory.
Chapter 7
El C:\My Inter estsAitificidl lntelligence\Loglc Piogramming
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R Pi Artificial Inteligence I 0-0 Knowledge Representation B G KR Formalisms
® O Frame Based Languages ! 0-0 Logic Based Languages G KIF G OIL G Ptotog ffl-G Object Oriented Languages B G KR Systems
B O Fiame Based Systems ± ■% | Logic Based Systems
I I Constraint Rules O Eclipse
O Logc Programming Conferences O Logc Programming Research Projects G Ptcfog ® G Rfrasorwrg Under Uncertanty S G SemanhcWeb O Computational Linguistics G Dogs j- G Beaided Collies G Cocker Spaniel ; Funny Stones
Size | Type
G Logic Programming Conferences File Fol
(G Eclipse File Fol
G Logic Programming Research Projects File Fol
G Constraint Rules File Fol
G Prolog File Fo
0 bytes My Computer
5 object(s) (Disk free space: 9.72 GB)
Figure 7.6 Directory-subdirectory taxonomy.
Understanding Taxonomies
In general, all you can say about the relationship between a parent and a child in a semantically weaker taxonomy is that it means is subclassification of: a fairly ill-defined semantics. But as we've seen in the semantically stronger taxonomies, the relationship can be the stronger subclass of relation. It is a semantically stronger taxonomy (using the subclass of relation) that is the backbone of conceptual models and ontologies.
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