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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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Note: Terms such as “action," “deliver," “location," and possible location values, units of measure, etc. have to be defined in an ontology that both agents know about. The ontology represents the meaning for these terms.
"((action (agent-identifier :name j)
(deliver package234 (loc 25 35))))"
The Agent J action about the delivery of a specific package package234 to a specific location identified by 25 35, has high priority.
Note: Terms such as “action," “deliver," “location," and possible location values, units of measure, etc. have to be defined in an ontology that both agents know about.
"((action (agent-identifier :name j)
(deliver package234 (loc 25 35)))
Expressing Ontologies Logically
(priority order678 high))"
As mentioned in the previous section, ontologies are usually expressed in a logic-based knowledge representation language, so that fine, accurate, consistent, sound, and meaningful distinctions can be made among the classes, instances, properties, attributes, and relations. Some ontology tools can perform automated reasoning using the ontologies, and thus provide advanced services to intelligent applications such as conceptual/semantic search and retrieval (non-keyword based), software agents, decision support, speech and natural language understanding, knowledge management, intelligent databases, and electronic commerce.
Chapter 8
As we saw in Chapter 7, an ontology can 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).
More technically, an ontology is both the vocabulary used to describe and represent an area of knowledge and the meaning of that vocabulary—that is, it is syntactically a language of types and terms that has a corresponding formal semantics that is the intended meaning of the constructs of the language and their composition. The recent computational discipline that addresses the development and management of ontologies is called ontological engineering.
Ontological engineering usually characterizes an ontology (much like a logical theory) in terms of an axiomatic system, or a set of axioms and inference rules that together characterize a set of theorems (and their corresponding formal models)—all of which constitute a theory (see Figure 8.7 and Table 8.4). In the technical view of ontological engineering, an ontology is the vocabulary for expressing the entities and relationships of a conceptual model for a general or particular domain, along with semantic constraints on that model that limit what that model means. Both the vocabulary and the semantic constraints are necessary in order to correlate that information model with the real-world domain it represents.
Figure 8.7 schematically attempts to show that theorems are proven from axioms using inference rules. Together, axioms, inference rules, and theorems constitute a theory.
Table 8.4 displays a portion of an ontology represented as axioms and inference rules. This table underscores that an ontology is represented equivalently either graphically or textually. In this fragment, the ontology is represented textually. The class-level assertions are in column one, labeled Axioms; these are asserted to be true. The representative Inference Rules (by no means all the inference rules available) are in column two. Finally, the Theorems are in column three. Theorems are hypotheses that need to be proved as being true. Once proved, theorems can be added to the set of axioms. Theorems are proved true by a process called a proof. A proof of a theorem simply means that, given a set of initial assertions (axioms), if the theorem can be shown to follow by applying the inference rules to the assertions, then the theorem is derived (validated or shown to be true).
Understanding Ontologies
Figure 8.7 Axioms, inference rules, theorems, theory.
The set of axioms, inference rules, and valid theorems together constitute a theory, which is the reason that high-end ontologies on the Ontology Spectrum are called logical theories. Table 8.4 displays axioms at the universal level, that is, the level at which class generalizations hold. Of course, we realize that part of an ontology is the so-called knowledge base (sometimes called fact base), which contains assertions about the instances and which thus constitutes assertions at the individual level.
Also in this example, we note that there are probably many more axioms, inference rules, and theorems for this domain. Table 8.4 just represents a small fragment of an ontology to give you an idea of its logical components.
Table 8.5 gives another example of an ontology, one that is probably of interest in electronic commerce. In this example, the ontology components are expressed in English, but typically these would be expressed as textually or graphically in a logic-based language as in the previous example. Note in particular that the single-rule example looks very similar to the last axiom in the first column of Table 8.4. This ontology example comes from electronic commerce: the general domain of machine tooling and manufacturing. Note that these are expressed in English but usually would be in expressed in a logic-based language.
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