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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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Understanding Ontologies
Entities: Metal working machinery, equipment and supplies, metal-cutting machinery, metal-turning equipment, metal-milling equipment, milling insert, turning insert, etc.
Relations: Subclass-of; instance-of; part-of; has-geometry; performs, used-on; etc.
Properties: Geometry; material; length; operation; UN/SPSC-code; ISO-code; etc.
Values: 1; 2; 3; "2.5 inches"; "85-degree-diamond"; "231716";"boring"; "drilling"; etc. Axioms/Rules:|f milling-insert(X) & operation(Y)& material(Z)=HG_Steel & performs(X, Y, Z), then has-geometry(X, 85-degree-diamond).
• Related Term RT
• Broader Term BT
• Narrower Term NT
• Part Of
• Arbitrary Relations
• Meta-Properties on Relations
Figure 8.9 Thesaurus versus ontology.
An ontology, however, does try to represent the complex semantics of concepts and the relations among concepts, their properties, attributes, values, constraints, and rules. But then, the purpose of an ontology is quite distinct from that of a thesaurus. An ontology does try to capture and represent the meaning of a domain, a set of domains, or the entire world, because it attempts to explicitly simulate the meaning that a human being has in his or her mental model of the domain, set of domains, or the world. Furthermore, an ontology is meant to be used directly by many kinds of software applications that have to operate on and so have knowledge about the domains represented by the ontology—including sometimes applications that have not yet been thought of. Finally, an ontology is meant to be extended, refined, and reused, traits that it shares with its semantically weaker cousin, the thesaurus. Unlike the thesaurus, however, an ontology tries to express precise, complex, consistent, and rich conceptual semantics.
Given this distinction between terms and concepts, how do ontological engineers actually develop the ontologies that contain the concepts? How do they decide what the concepts and relations of a particular domain are? How do they discover the principles holding for those concepts and relations?
Troelstra (1998) asks those same questions about mathematics. Since ontological engineering generally adopts the formal methods of mathematics and logic, we think the following quotation from Troelstra (1998, pp. 1-2) is appropriate here.
Chapter 8
Given an informally described, but intuitively clear concept, one analyzes the concept as carefully as possible, and attempts to formulate formally precise principles characterizing the concept to a greater or lesser extent.
Although space precludes us from delving too deeply into ontological engineering as a technical discipline, we will introduce some semantic concepts related to ontologies that are important to ontological engineers.
Important Semantic Distinctions
This section is an introduction to some of the semantic distinctions and issues that are useful to know when learning about ontologies:
■■ Extension
■ Intension
■ Meta and object levels of representation ■■ Ontology and semantic mapping
Extension and Intension
Typically, ontologies make a distinction between intension and extension. The same distinctions hold of other models in other modeling languages; however, other models typically don't make these formal distinctions—though they should.
Ontologies provide two kinds of knowledge:
■ About the class or generic information that describes and models the problem, application, or, most usually, the domain
■ About the instance information—that is, the specific instantiation of that description or model
In the database and formal/natural language worlds, the first type of knowledge is the intension and the second is the extension. In the database world, a schema is the intensional database, whereas the tuples of the database constitute the extensional database. In the formal/natural language worlds, a description or specification is an intension, whereas the actual objects (instances/individuals) in the model (or world) for which the description is true are in the extension.
A definite description in natural language, for example, is a nominal—that is, a noun compound, such as "the man in the hat" or "the current President of the United States," which is a description that seemingly picks out a definite individual in the world or a particular context, indicated by the use of the definite article "the." The definite description "the man in the hat" therefore picks
Understanding Ontologies
out the man, whoever he is, who happens to be wearing a hat in the current context of our conversation in a particular room. Let's say that we are at a party. I am in a conversation with you when I suddenly point to an individual across the room and say, "I think you know the man in the hat over there." You look, perhaps squinting your eyes, and reply, "Is that Harry?" If the man in the hat is indeed our mutual friend Harry Jones, I'll respond, "Yes." The intension in this case is "the man in the hat." The extension is "Harry Jones." Harry Jones is the individual for which it is true that he is "the man in the hat." The property of being the man in the hat could actually apply to countless individuals in other contexts. That intensional description, "there is someone who has the property of being a man wearing a hat," could pick out many specific individuals in different contexts. Whichever individuals that description applies to in a specific context is said to constitute the extension of that intensional description.
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