Knowledge representation procedural

Knowledge machine (KM) is a frame-based knowledge representation langnage similar to KRL and other KL-ONE representation languages such as Loom and CLASSIC [13-15], In KM, a frame denotes either a class (i.e., type) or an instance (i.e., individual). Frames have slots, or binary predicates, in which the fillers are axioms about the slot s value. These axioms have both declarative and procedural semantics, allowing for procedural inference. 

Most expert-system development efforts will also follow the five phases of the conventional life cycle with heavy emphasis on knowledge acquisition and representation in the design phase. Several of the V V procedures outlined in Table I for conventional software life cycle can be applied in ES V V. Table II provides a list of some applicable procedures. Many procedures are simply a part of any software development effort, but are many times overlooked in ES efforts. For example, ES developers oftentimes do not develop adequate requirements documentation. They have difficulty in tracing knowledge representation structures (rule paths, frames, etc.) back to design documents which in turn makes... 

Knowledgebase oriented Ill-structured problem representation, logic (declarative) programming Procedural execution Knowledge rep esentation handling, inference engine with procedural attachment, e.g., DICEtalk with percept knowledge representation... 

The domain module represents what is being teached and contains usually a set of facts and/or rules representing the knowledge we have about the domain. Sometimes this part of the system is also responsible for the automatic generation of examples. A lot of attention has been paid to the problem of domain knowledge representation, the solutions ranging from declarative (frame-like) to procedural ones (production rules). 

Abstract. This article describes our current work on the combination of an ontology-based knowledge representation and formal analysis procedures. We use formalized system engineering knowledge and partial architectural information (induced by a set of requirements) to formalize natural language requirements and to identify inconsistencies based on this formahzation. Our analysis combines requirements specified by patterns and an ontology-based product breakdown structure. As an example, we identify inconsistencies between Mean Time Between Failure (MTBF) specifications of systems and their subsystems. 

The above discussion, thus, has made somewhat more precise the sense of the limiting procedure involved in writing down the representation (10-216) for the scattering amplitude. This representation also makes clear that a knowledge of Green s function... 

Atoms and their symbols were introduced in Chap. 3 and 1. In this chapter, the representation of compounds by their formulas will be developed. The formula for a compound (Sec. 4.3) contains much information of use to the chemist. We will learn how to calculate the number of atoms of each element in a formula unit of a compound. Since atoms are so tiny, we will learn to use large groups of atoms—moles of atoms—to ease our calculations. We will learn to calculate the percent by mass of each element in the compound. We will learn how to calculate the simplest formula from percent composition data, and to calculate molecular formulas from simplest formulas and molecular weights. The procedure for writing formulas from names or from knowledge of the elements involved will be presented in Chaps. 5. ft. and 13. 

We have not attempted to make the computer do the job of auto-r matically finding the fundamental laws of chemistry from a compilation of individual facts. Rather, we have explicitly built into the computer specific models that we believe can represent the structure of chemical information. We were guided in this endeavor by concepts derived by the chemist and have tried to develop models and procedures that quantify these concepts. In doing so we have put more emphasis on the acquisition and representation of knowledge than on problem-solving techniques. In any expert system the quality of the knowledge base is of primary and desicive importance. 

There are still some other requirements that a representation should fulfill, but they are mainly of more specific nature. There is no such representation that would satisfy all requirements, hence, the representation must be selected in a kind of trial-and-error procedure guided by a good spectroscopic knowledge. 

Object-oriented systems use the concept of reusable entities that contain both the data and procedures relevant to the object, and thus eliminating the separation of knowledge and reasoning found in expert systems. In object-oriented systems computation is behavior simulation of real-life systems. Once certain classes of objects are created, they can be reused to create other objects and properties with interface and behavior. The self-contained character of objects is known as encapsulation. Inheritance allows derivation of new objects from existent ones, and encapsulation defines the limits of services an object can provide to other objects. An example of this system is provided by GENERA,31 and a schematic representation is given in Figure 5.2. 

Of course, the comparison with experiments by a single technique cannot prove that a model is the unique realistic representation of molecular motions. But, in Sec. 4, we give some examples in which the aforementioned procedure has been useful to select between the different expressions proposed in literature and improve our knowledge of chain dynamics. 

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