1.Intruduction
The paper (John Molopoulos,1981) is a brief overview of terminology and related issues on knowledge representation.
Knowledge Representation is a central problem in Artificial Intelligence (AI) today. Its importance stems from the fact that the current design paradigm for "intelligent" systems stresses the need for expert knowledge in the system along with associated knowledge-handling facilities.
The basic problem of KR is the development of a sufficiently precise notation for representing knowledge. We shall refer to any such notation as a (knowledge) representation scheme. Using such a scheme one can specify a knowledge base consisting of facts.
2.A Taxonomy of Representation Schemes
Representation schemes have been classified into declarative and procedural ones . The paper subdivide declarative schemes into logical and (semantic) network ones.
2.1
Logical Representation schemes is such schemes employ the notions of constant, variable, function, predicate, logical connective and quantifier to represent facts as logical formulas in some logic (First or Higher Order/Multi- valued/Modal/Fuzzy etc.). A knowledge base, accord- ing to this view, is a collection of logical formulas which provides a partial description of a world.
The advantages of logical representaton schemes is 1) the availability of inference rules in terms of which one can define proof procedures. 2) It's an availability of a clean, well-understood and well-accepted formal semantics. 3)the simplicity of the notation employed which leads to knowledge base descriptions that are understandable.
The main drawbacks logical representation schemes is 1)A major drawback of logical schemes is the lack of organizational principles for the facts con-stituting a knowledge base. 2) A second drawback is the difficulty in repre- senting procedural and heuristic knowledge.
2.2
Semantic network representation schemes, attempt to describe a world in terms of objects (nodes) and binary associations (labelled edge). According to a network representational view, a knowledge base is a collection of objects and asso- ciations, or a directed labelled graph, and modifications to the knowledge base occur through the insertion/deletion of objects and the manipulation of associations.
Due to its nature, network schemes address directly issues of information retrieval since associations can be used to define access paths for traversing a network knowledge base. Another impor- tant feature of network schemes is the potential use of primitive association types for the organization of a knowledge base. A third advantage is the obvious graphical representation of network knowledge bases which enhances their understandability.
A major drawback of network schemes has been the lack of a formal semantics and a standard terminology.
2.3
Procedural schemes view a knowledge base as a collection of procedures expressed in some language.
Procedural schemes allow the specification of direct interactions between facts thus eliminating the need for wasteful searching. On the other hand, a procedural knowledge base, like a program, is difficult to understanding and modify.
2.4
A frame is a complex data structure for representing a stereotypical situation such as being in a certain kind of living room or going to a child's birthday party. The frame has slots for the objects that play a role in the stereotypical situation as well as relations between these objects. Attached to each frame are different kinds of information such as how to use the frame, what to do if something unexpected happens, default values for its slots etc.
3. Distinguishing Features of Representation Schemes
3.1 Mutiple Use of Facts
There are some operations could be performed on konwledge bases. They are Inference, Access and Matching. Inference is to deduce new facts from known facts. Access is to finish a questioning -answering applications on knowledge base. Matching is a operation on the relationship set of knowledge for different purposes and in different forms.
3.2 The Incompleteness of Knowledge Base
The knowledge base is always incompleteness though it is always been improved.
3.3 Self Knowledge
Facts which describe the form or allowable configurations of other facts (e.g. type definitions) are an important kind of self knowledge.
A second kind of self knowledge involves the ability of a system to answer elementary questions about its actions, or about the strategies it uses to perform some task.
The paper (John Molopoulos,1981) is a brief overview of terminology and related issues on knowledge representation.
Knowledge Representation is a central problem in Artificial Intelligence (AI) today. Its importance stems from the fact that the current design paradigm for "intelligent" systems stresses the need for expert knowledge in the system along with associated knowledge-handling facilities.
The basic problem of KR is the development of a sufficiently precise notation for representing knowledge. We shall refer to any such notation as a (knowledge) representation scheme. Using such a scheme one can specify a knowledge base consisting of facts.
2.A Taxonomy of Representation Schemes
Representation schemes have been classified into declarative and procedural ones . The paper subdivide declarative schemes into logical and (semantic) network ones.
2.1
Logical Representation schemes is such schemes employ the notions of constant, variable, function, predicate, logical connective and quantifier to represent facts as logical formulas in some logic (First or Higher Order/Multi- valued/Modal/Fuzzy etc.). A knowledge base, accord- ing to this view, is a collection of logical formulas which provides a partial description of a world.
The advantages of logical representaton schemes is 1) the availability of inference rules in terms of which one can define proof procedures. 2) It's an availability of a clean, well-understood and well-accepted formal semantics. 3)the simplicity of the notation employed which leads to knowledge base descriptions that are understandable.
The main drawbacks logical representation schemes is 1)A major drawback of logical schemes is the lack of organizational principles for the facts con-stituting a knowledge base. 2) A second drawback is the difficulty in repre- senting procedural and heuristic knowledge.
2.2
Semantic network representation schemes, attempt to describe a world in terms of objects (nodes) and binary associations (labelled edge). According to a network representational view, a knowledge base is a collection of objects and asso- ciations, or a directed labelled graph, and modifications to the knowledge base occur through the insertion/deletion of objects and the manipulation of associations.
Due to its nature, network schemes address directly issues of information retrieval since associations can be used to define access paths for traversing a network knowledge base. Another impor- tant feature of network schemes is the potential use of primitive association types for the organization of a knowledge base. A third advantage is the obvious graphical representation of network knowledge bases which enhances their understandability.
A major drawback of network schemes has been the lack of a formal semantics and a standard terminology.
2.3
Procedural schemes view a knowledge base as a collection of procedures expressed in some language.
Procedural schemes allow the specification of direct interactions between facts thus eliminating the need for wasteful searching. On the other hand, a procedural knowledge base, like a program, is difficult to understanding and modify.
2.4
A frame is a complex data structure for representing a stereotypical situation such as being in a certain kind of living room or going to a child's birthday party. The frame has slots for the objects that play a role in the stereotypical situation as well as relations between these objects. Attached to each frame are different kinds of information such as how to use the frame, what to do if something unexpected happens, default values for its slots etc.
3. Distinguishing Features of Representation Schemes
3.1 Mutiple Use of Facts
There are some operations could be performed on konwledge bases. They are Inference, Access and Matching. Inference is to deduce new facts from known facts. Access is to finish a questioning -answering applications on knowledge base. Matching is a operation on the relationship set of knowledge for different purposes and in different forms.
3.2 The Incompleteness of Knowledge Base
The knowledge base is always incompleteness though it is always been improved.
3.3 Self Knowledge
Facts which describe the form or allowable configurations of other facts (e.g. type definitions) are an important kind of self knowledge.
A second kind of self knowledge involves the ability of a system to answer elementary questions about its actions, or about the strategies it uses to perform some task.
Comments