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

Reasoning with Semantic Networks

Semantic networks can be very difficult to reason from, as an inference engine must understand what type of link exists between nodes within the semantic network, and there are no constraints on allowable links.

To understand the semantic network that describes Slither, you need to understand the links, i.e., the words ‘is a’, ‘eats’, ‘vegetarian’, etc. If an inference engine were to reason with this network, then it must have some understanding of these words and this is difficult to build in.

Semantic networks are particularly good at representing knowledge in the form of hierarchies. They can also represent complex causal relationships—though the diagrams can become large and complex. Their explicit links and visual diagrams can make knowledge quite clear. Much of the reasoning with semantic networks is in the form of deducing indirect links between concepts based on direct links explicitly stated. This is difficult for an inference engine to achieve in practice however, because it needs to understand the links and the conclusions depend on successfully searching the network. For this reason, semantic networks are often used only as a communication tool between the knowledge engineer and the domain expert and after the knowledge has been obtained it is then converted into another format that is easier to process.

Advantages of Semantic Networks

The advantages of semantic networks to represent knowledge include:

They tend to be a powerful and adaptable method of representing knowledge because many different types of object can be included in the network.

The network is graphical and therefore relatively easy to understand.

Can be used as a common communication tool between the knowledge engineer and the human expert during the knowledge acquisition phase of designing an ES.

Disadvantages of Semantic Networks

The disadvantages of using semantic networks to represent knowledge include:

It can be difficult to show all the different inference situations using a network.

They are less reliable than other knowledge representation techniques because inferring becomes a process of searching across the diagram.

Diagrams can become very complex.

The wide range of possible kinds of links and the ways they might combine to form indirect linkages, plus the large number of concepts usually included in a

semantic network make this form of representation susceptible to a combinatorial explosion.

Semantic networks have difficulty associating procedural knowledge with the facts represented by the network and, since they lack any means of bundlingrelated facts into associated clusters, usually result in a proliferation of many different concepts and linkages producing a complex representation that may require extensive search operations to reach conclusions.

Limitations of Semantic Networks

Semantic networks as a representation of knowledge have been in use in Artificial Intelligence for many years. Some of the earliest uses of a nodes-and-links approach were in the work of Quillian (1968) and Winston (1975), where semantic networks were used as models of associative memory. Quillian (1968) focused on how natural language is understood and the capturing of meaning by a machine. Winston (1975) focused on machine learning and structural descriptions of an environment.

Creations and uses of semantic networks have led to a number of epistemological problems, which numerous researchers have attempted to address these problems. Barr and Feigenbaum state that:

In semantic network representations, there is no formal semantics, no agreed-upon notion of what a given representational structure means, as there is in logic, for instance.

Semantic networks do tend to rely upon the procedures that manipulate them.

The system is limited by the user’s understanding of the meanings of the links in a semantic network. Links between nodes are not all alike in function or form. We therefore need to differentiate between links that assert some relationship and links that are structural in nature.

Summary

In this section you have learned about the role of semantic networks in knowledge representation. In the process, you discovered the limitations of such an approach and why semantic networks can be used as a communication tool between the knowledge engineer and the domain expert.

Current Research Links

Semantic Research

http://www.semanticresearch.com/

Answer to Self-Assessment Question

You may have referred to such characteristics of current knowledge representation technology as:

the limiting of the questions that can be asked in order to allow the computer to answer reliably, or at all

the centralisation involving the requirement to share exactly the same definition.

On the other hand, you may have noted the potential improvements claimed for approaches incorporating XML, RDF, URIs, etc.

SECTION 5: FRAMES

Introduction

This section provides an introduction to how knowledge can be represented in ESs using frames.

Objectives

By the end of the section you will be able to:

explain how frames can be used to represent knowledge

describe how ontologies can be used to represent knowledge.

Frames

Frames are a simplified version of a semantic network where only ‘is a’ relationships apply.

Frames provide a method of storing knowledge, collecting specific information about one object in an ES. In essence they allow both data and procedures to be included within one structure.

An example frame for a coffee mug object can be drawn as follows:

Coffee mug FRAME

Within that structure, slots (i.e., rows) can:

store details of each data object

provide links to other frames

contain procedural code, linking to other applications to obtain data or write data

indicate whether or not certain properties of each object are needed within that frame.

In practice, three different types of slots are used:

1.Named slots having a standard filler value of certain data items. For example, the slot for number of wheels in a car frame will have a default value of four. This can be overwritten where the specific type of car being described (such as a three-wheel car) does not meet this default value. Range values can also be specified, e.g. the size must be small, medium or large.

2.Slots showing relationships using the term IS A. For example, a car is a motor vehicle. The IS A motor vehicle slot will therefore link the frame for car with a frame describing the basic features of a motor vehicle.

3.Slots contain procedural code. For example, the number of miles that a car can travel, i.e., its range, is determined by the current petrol stored in its tank and by the engine size. The slot for range can therefore store procedural code to calculate the range (if needed) based on the slots for current petrol and engine size. This procedural code is called a demon (in this case an if needed demon) and is activated automatically if a value for range is needed.

Activity 20

Complete the following frame for a car based on the information provided above. Remember that cars are available in a range of sizes and that they are part of the overall set of motor vehicles.

Car FRAME

IS A

MANUFACTURER

CAN TRAVEL ON ROADS

NUMBER OF-WHEELS

SIZE

PETROL TANK CAPACITY

CURRENT FUEL

ENGINE SIZE

RANGE

Levels Within Frames

The levels of information within frames are:

The highest level in a frame is literally FRAME, which stores the name of the specific frame.

Below this there are SLOTS, with each slot providing information on one of the attributes of that frame.

Within the slot, the FACET provides detail on each attribute. This detail may include a value, ranges that can be applied to the attribute, default values or calculated values including demons.

Finally, the DATA provides specific information about each attribute, such as the NUMBER OF WHEELS being 4 in a frame describing a motor vehicle.

Inheritance

One of the main advantages of using frames is the principle of inheritance. This means that frames can inherit the attributes of other frames, in a hierarchical structure. For example, a frame for a cup can provide some basic attributes in a number of slots about that object. These attributes can be given to other objects that share those attributes.

Figure 4.6 shows the attributes of a mug being applied to two other drinking receptacles, namely a tea mug and a coffee mug.