12-10-2013_09-24-50 / [Simon_Kendal,_Malcolm_Creen]_Introduction_to_Know(BookFi.org)

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Three-wheeled Car FRAME

Remember that the NUMBER OF WHEELS slot does not take on the default value in this situation but is overwritten with the new value of 3.

Advantages of Using Frames

The advantages of using frames are that they can:

be represented in the form of a table, making the information easy to read and assimilate.

store default values. So where a frame has an IS A slot, that frame is linked to another frame containing generic information for a particular group of frames. The generic information is used to fill slots in the frame, rather than having to enter all of these manually.

use default values in the reasoning process. If later, the default value is found to be incorrect, then the system can overwrite the default value and then run through its reasoning again to see if this changes any conclusions it reached earlier. In this way, frames can be used to mimic default reasoning, which humans often use.

be structured hierarchically and thus allow easy classification of knowledge.

reduce complexity by allowing a hierarchy of frames to be built up.

clearly document information using common formats and syntax.

combine procedural and declarative knowledge using one knowledge representation scheme.

constrain allowed values, or allow values to be entered within a specific range.

enable demons to be specified that when triggered perform some automatic procedure (often used to ensure slot values are consistent).

Disadvantages of Using Frames

The disadvantages of using frames are that they:

can be inefficient at runtime because they do not provide the most efficient method to store data for a computer.

can lead to ‘procedural fever’, that is the apparent requirement to focus on making appropriate procedures rather than checking the overall structure and content of the frames.

require care in the design stage to ensure that suitable taxonomies, i.e., agreed structures for the terminology are created for the system.

Before closing this chapter, we need to look at an additional approach to storing knowledge in a KBS, one that is of current and increasing interest and quite closely related to frames.

Ontologies

In philosophy, the term ‘ontology’ refers to ‘a particular theory about the nature of being or the kinds of existence’.

From a knowledge engineering perspective, the term ontology is often used as a synonym for the terminology in some domain.

Activity 22

A number of researchers in the knowledge engineering field have suggested the following knowledge engineering-specific definitions of ontology. Suggest what implications these definitions have for knowledge representation.

An ontology is an explicit specification of a conceptualization (Gruber, 1994). An (AI-) ontology is a theory of what entities can exist in the mind of a knowledgeable agent (Wielinga and Schreiber, 1993).

An ontology for a body of knowledge concerning a particular task or domain, describes a taxonomy of concepts for that task or domain that define the semantic interpretation of the knowledge (Alberts, 1993).

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You may have been able to suggest the following implications for knowledge engineering of the term ontology defined above:

Gruber’s definition, though not explicitly stated, suggests that an ontology is a meta-level description of a knowledge representation.

Wielinga and Schreiber’s definition emphasises that we want to apply the notion of ontology to all knowledgeable agents, including humans. Since different knowledgeable agents will often have different symbol-level representations, it is convenient to formulate ontologies at the knowledge level. Ontologies can therefore be used as mediators between knowledge as it is understood by a domain expert and knowledge as it is represented in a KBS.

Alberts’s definition emphasises that it is not the terminology itself that constitutes the ontology but the semantic interpretation of the terms. Another important aspect of this definition is that ontologies can be specific for tasks or for domains. That is, both the domain and the task at hand may affect the ontology.

Combining the above definitions results in the following definition:

Ontologies can be classified according to the amount and type of structure of the conceptualisation and the subject of the conceptualisation. In relation to the former, it is possible to distinguish three categories:

Terminological ontologies, such as lexicons, specify the terms that are used to represent knowledge in the domain.

Information ontologies which specify the record structure of databases.

Knowledge modelling ontologies specify conceptualisations of the knowledge and usually have a richer internal structure than information ontologies. They are often customised for a particular use of the knowledge they describe.

Within the context of KBS development, knowledge modelling ontologies are the most useful. A detailed description of the use of this type of ontology applied to electronic medical records is available at: http://www.cs.man.ac.uk/ mig/ftp/pub/papers/alr-foundations.pdf.

By creating an ontology within an ES we can define the limitations of the knowledge stored and thus hope to combat brittleness.

Other Knowledge Representation Issues

When selecting a suitable knowledge representation scheme the most natural form of representation should be aimed for. This may involve using a combination of different techniques to provide advantages of each without the disadvantages of any. It is possible, for example, to use within one KBS, a mixture of the rules and frames, if both of these representation schemes are supported by the development tool.

It is also important to work to the strengths of that knowledge representation scheme, and understand the tools used.

Both the cost and complexity of using a combination of knowledge representation schemes must be considered and measured against the gains in flexibility.

It is often necessary to break the problem into parts. Complexity tends to increase with the problem size, and decomposition techniques can produce efficient KBSs. A 2000 rule KBS will be too much for many computers to cope with. However, a KBS which consists of 20 rule sets, each one having 100 rules can be processed efficiently.

Appropriate planning for the knowledge representation scheme used must take place involving the definition of the appropriate types of knowledge representation scheme that will be used when organising the knowledge.

Tools that best support the different representations need to be identified. If none of the available tools provide exactly what is required, then the next best possible choices must be evaluated. This includes identifying their strengths so that these can be applied to the task.

A knowledge-based structure should take maximum advantage of the knowledge representation scheme. The problem should not be conceptualised in procedural terms as this can result in inefficient code.

No single knowledge representation method is ideally suited for all problems. As a knowledge engineer you may be well advised to sacrifice the goal of uniformity and explore the possible benefits of multiple knowledge representation schemes.

Current Research Links

Using Explicit Ontologies in KBS Development

htp://ksi.cpsc.ucalgary.ca/IJHCS/VH/VH1.html

Natural Language & Knowledge Representation Research Group

http://tigger.cs.uwm.edu/ nlkrrg/

KR, Inc.

Principles of Knowledge Representation and Reasoning, Incorporated

http://www.kr.org/

Knowledge Representation Laboratory

http://kr.cs.ait.ac.th/

Self-Assessment Questions

Question 1

Represent the following facts as a set of frames, using the notation described earlier:

‘The aorta is a particular kind of artery that has a diameter of 2.5 cm. An artery is a kind of blood vessel. An artery always has a muscular wall, and generally has a diameter of 0.4 cm. A vein is a kind of blood vessel, but has a fibrous wall. Blood vessels all have tubular form and contain blood’. (With thanks to Alison Cawsey [1998] for permission to use this question.)

Note that the FORM and CONTAINS BLOOD slots can be inherited from the BLOOD VESSEL frame and therefore do not need to be repeated in the VEIN and ARTERY frames.

Answer 2

Semantic networks could be used for this problem but inference, i.e., the diagnosis, would be complex. Therefore, it may be better to restrict the use of semantic networks to the knowledge acquisition phase of the project, i.e., use them as a communication tool between the knowledge engineer and expert. After this stage the knowledge would be converted into another format.

Frames are much simpler being restricted to is a style relationships. Frames would provide a good method of storing the data about the pets and it would be easy to categorise them into suitable hierarchies, e.g. small mammals, reptiles, etc. Similar pets could inherit data and characteristics from frames higher up in the hierarchy, thus simplifying the data stored (food etc.). Data on pet ailments could be stored as procedural code (demons) associated to particular slots, e.g. a cat frame could have a slot called eating disorders and another called infections. However, encoding this knowledge as procedural code could get messy so let us consider rules as an alternative to frames.

With rules storing details of the pets could be complex and difficult to visualise however defining rules to describe pet problems and the associated symptoms would be quite easy. Thus, using rules storing details of the actual pets would be complex but invoking inference to diagnose problems would be easy.

Perhaps the best solution would therefore be to use a combination of rules and frames for this problem.

References

Inference, Forward and Backward Chaining

Cawsey, A. (1998). The Essence of Artificial Intelligence. Prentice-Hall: London, England. Wolverton, M. (1995). Presenting significant information in expert system explanation. In Seventh Portugese Conference on Artificial Intelligence (EPIA95), Portugal,

October 1995.

Semantic Networks

Quillian, M. (1968). Semantic memory. In Minsky, M. (editor), Semantic Information Processing. MIT Press: Cambridge, MA, pp. 216–270.

Alberts, L. K. (May, 1993). YMIR: An Ontology for Engineering Design. Thesis, University of Twente, Enschede, The Netherlands.

Winston, P. W. (1975). Learning Structural Descriptions from Examples, in The Psychology of Computer Vision, Winston P. (ed.), McGraw Hill, New York, 157–209.

inefficient to run and difficult to maintain. In much the same way as a large program is split into functions and procedures, it is desirable to split a large knowledge base into several smaller knowledge bases. Shells cannot usually support this.

Shells usually have limited flexibility when inferencing (e.g. backward chaining only) and it may be that for part of a problem forward chaining would be ideal while for another part of the same problem backward chaining would be ideal.

Shells tend to be inflexible or at least difficult to modify. This means that knowledge may not always be entered in exactly the correct format in the ES.

The knowledge domain may be simplified within the system because the shell cannot represent the full complexity of the domain.

It is easy to let the shell dictate the format of the system, rather than choose the ideal format and then find a tool that supports this format.

Domain-Specific Shells

Domain-specific shells are simply ES shells that have been written to represent knowledge within a specific domain. For example, shells are available to assist with the help desk, scheduling and configuration of systems. The shells provide a specific user-interface to assist with the trapping of knowledge, but tend to be more expensive than more general ES shells. Case-based reasoning (CBR) Express is one example of a domain-specific shell.

Examples of ES Shells

Expert system shells include:

Crystal

JESS.

Crystal is an old ES shell that has the following facilities:

a relatively simple inference engine that supports a knowledge base of rules

facilities for creating a graphical user-interface

a backward chaining inference engine

automatic, but basic, explanation features

can interface with text files, databases, spreadsheets and C code.

While Crystal is not a modern tool it was used in a variety of real world applications. This included an ES created by a bank to assess mortgage applications.

JESS by comparison is a modern ES shell written in Java. Unlike Crystal it was not designed for the creation of standalone ESs but is instead designed to support the