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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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^ organization, organisation—(a group of people who work together)
^ social group—(people sharing some social relation)
^ group, grouping—(any number of entities (members) considered as a unit)
Sense 2: bank—(sloping land (especially the slope beside a body of water); "they pulled the canoe up on the bank"; "he sat on the bank of the river and watched the currents")
^ slope, incline, side—(an elevated geological formation; "he climbed the steep slope"; "the house was built on the side of the mountain")
^ geological formation, geology, formation—(the geological features of the earth)
^ natural object—(an object occurring naturally; not made by man)
^ object, physical object—(a tangible and visible entity; an entity that
can cast a shadow; "it was full of rackets, balls, and other objects")
^ entity, physical thing—(that which is perceived or known or inferred to have its own physical existence (living or nonliving))
Sense 3: bank—(a supply or stock held in reserve for future use (especially in emergencies))
Figure 7.9 WordNet entry for bank: First three word senses and their hypernymic taxonomies conceptual model.
The if part of the rule is sometimes called the antecedent; the then part is called the consequent. Rules are like axioms or constraints. Although we briefly talk about axioms in the next section, most of the discussion will have to wait until Chapter 8. These logical rules are related to rules you may be more familiar with: the production rules of expert systems. Production rules are condition-action rules of the form:
■ If condition X is true, then perform action Y.
where X again is an arbitrarily complex set of conditions that hold (or are true) in the current state of the environment, and Y is an arbitrarily complex set of actions.
Understanding Taxonomies
165
Actions here include setting specific values to variables, asserting variables (conditions) to be true, or executing other production rules, in a rule-chaining style sometimes called forward-chaining (or top-down or right-to-left inference, the prototypical reasoning method employed by expert systems). In other words, if the antecedent of the production rule is true, then the actions of the consequent are executed, thereby changing the state of the environment, and so possibly enabling the conditions of other rules in the entire rule set to become true, thus causing them to fire (become activated). Other common synonyms for production rules are demon and trigger, the latter sometimes used as a mechanism in database technology for changing the state of a database.
The opposite type of rule execution in expert systems is called backward-chaining (bottom-up, right-to-left, goal-directed reasoning), where the consequent's goal states are considered true, and so its conditions would generate new goals, with the new goals matching the consequents of other rules.5 In general, the production rules of expert systems are essentially nonlogical implementations of inference—that is, they simulate inference. Although production rules are still in use today, in practice, more modern knowledge technologies (such as ontological engineering, which we discuss in Chapter 8) employ logical rules in true logical inference.
In a conceptual model, it truly is possible to define and express the subclass of relation between a parent class and a child class. Object-oriented programming modeling languages such as UML (and tools such as Rational Rose that use UML) are rich enough to express the semantics of the subclass of relation between two given classes.6 What is also important is that the definitions of a class, superclass, and subclass be semantically well specified at the metamodel level so that the object-model level classes such as Person and its subclass Employee can be well specified semantically. The object-model level is the level that we are interested in. It is the level at which we construct our domain and system models. The meta-model level is the level that defines the constructs such as class, relation, and attribute that we will use at the objectmodel level to define our content models. The meta-model level is often the level where the conceptual modeling language (such as UML) itself is defined. What is defined at the modeling language level enables us to express things in that language (i.e., construct our own models using the language) at the object level. This notion of meta level and object level can be confusing, so it is a topic that we will return to in the next chapter when we look at ontologies.
5 For a more detailed description of expert systems and their problems, see Obrst and Liu (2003), pp. 113 to 116.
6 For readers unfamiliar with the object-oriented programming paradigm, we suggest Graham (2000) and Rumbaugh et al. (1991). For general information on and specifications of UML, see http://www.uml.org/. For information on Rational Rose and UML, see http://www.rational. com/uml/index.jsp.
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