Semantic networks: visualizations of knowledge

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Semantic networks: visualizations of knowledge

Roger T. Hartley and John A. Barnden

n conferences and in the literature,

complexity theory in computer science. Many ex

written, and yet it is our belief that none of them

b& between their use as a formal scheme for knowledge

use as an informal tool for thinking. In our

. i.._ . ( ^

T he history of the development of semantic networks is

well known (for an introduction to semantic networks, see

Box 1). Both Sowa’ and Lehmann’ have expounded in ex-

cellent scholarly fashion as to their origins in the study of

language. Their later development as a tool for representing

knowledge is also well knowr?, as is their role in building

computerized inference systemPO. Indeed, the triad of in-

telligent thought, logic and language will never be far from

our discussion. From all these sources we learn that seman-

tic networks have three main attributes:

(1) They originate in the conceptual analysis of language.

(2) They can have an expressiveness equivalent to first-

order logic, at least (although many do not).

(3) They can support inference through an interpreter

that manipulates internal representations.

Many people would go further and say that semantic net-

works are indistinguishable from formal logic representa-

tions”. However, there is something missing here. The visual

aspect of the semantic network idea is clearly important. As

Sowa says: ‘network notations are easy for people to read”

and this pragmatic aspect ofthe formalism cannot be ignored.

According to Sowa: ‘graphs...can keep all the information

about an entity at a single node and show related information

by arcs connected directly to that node”. In contrast, in

symbolic logic notations: ‘the scattering of information not

only destroys the readability of the formula, but also obscures

the semantic structure of the sentence from which the formula

was derived. So the battle is joined! The visual aspects of the

semantic network notation are preferred (at least by Sowa)

over the arcane, but more traditional notation of symbolic

logic. Interestingly, this traditional notion was invented by

C.S. Peirce before he abandoned it in favor of a diagram-

matic form’a.

In this paper, we hope to show that this argument is only

one component of a larger, more complex one involving the

nature of semantics; we will also show how different notations

can lead to different systems with different pragmatic uses.

Meaning

The design and use of a knowledge representation revolves

around the business of meaning. Actually, one spin-off from

studies in natural language provides a good start, namely,

the meaning triangle of Ogden and Richards”. The triangle

relates objects in the real world, concepts that correspond to

Copyright 0 1997, Elsevier Science Ltd. All rights reserved. 1364-6613/97/$17.00 PII: 51364-6613(97)01057-7

Trends in Cognitive Sciences - Vol. 1. No. 5, August 1997

Hartley and Barnden - Semantic networks

Box 1. What is a semantic network?

Although this question ought to be easy, in fact, there is much

disagreement in the literature. The common elements are

clear, however. A semantic network has three characteristics:

(1) A way of thinking about knowledge in which there are

concepts linked by relationships. Concepts can be concrete,

such as objects, or abstract, such as states ot actions. ‘RFX is

a concrete object, ‘DOG’ is abstract. Relationships are usually

binary. ‘HAS’ relates ‘DOG’ and ‘FUR’. A semantic network

is usually thought of as declarative, i.e. it can capture the

meaning of a set of true facts. It is true that ‘Rex is a dog’.

(2) A diagrammatic representation comprising some com-

bination of boxes, arrows and labels. Typically boxes repre-

senting concepts are labeled with a word corresponding to

the concept, usually a noun for objects and a verb for actions.

The arrows are labeled with the relationship between the two

concepts at either end of the arrow. The direction of the

attow shows the directionality, if any, of the relation, i.e.

‘dog has fur’ not ‘fur has dog’. They may contain taxonomic

links to facilitate reasoning using inheritance and default

properties, or exceptions. For example, Rex ‘IS-A’ (instance

of the class ofJ dog; dogs have fur, therefore, Rex has fur.

(3) A computer representation that allows database-like activity

and a variety of inferencing techniques using algorithms

that operate on the representations. Many systems are im-

plemented using Lisp ot Prolog, but there is no inherent ad-

vantage in using any language as the directed graph, whicR

is the closest abstract data type to the ideas in a semantic

network, is not supported by any common language as a

built-in type. Most people agree that a semantic network is

meaningless without this algorithmic interpretation.

HAS FUR

Some people say that the diagrams themselves are the se-

mantic networks, but we think it is mote accurate to say that

the diagrams represent the semantic network, which is really

a network of concepts as held by a cognitive agent. The fact

that we can draw them as diagrams, and represent them in

computers, makes them extremely useful tools for cognitive

psychology and much of artificial intelligence. As we shall

see, they also have uses in more informal settings, when they

are often called semantic maps.

these objects and the symbols that languages use to refer to

them. Ogden and Richards’ point, echoed by many follow-

ers, was that language only acquires meaning through some

cognitive agent. In the triangle, there is no strong link be-

tween symbol and object. In mathematical terms, this map-

ping only exists as a composition of the mappings ‘symbol’

- ‘concept’ and ‘concept’ + ‘object’ (Fig. 1A).

It is no coincidence that the meaning triangle follows a

visual metaphor. In fact, it is itself a semantic network that

utilizes a number of features displayed by more comprehen-

sive systems. It is worth while spending a few moments

looking at the triangle, and seeing why it is a good way to

convey the desired information. Firstly, the triangle is always

drawn as equilateral, thus giving equal status to the nodes.

A

__________________--_____I_____

I object pq

B

I .object lconceptl

Fig. 1 Examples of the meaning triangle. (A) Normal, (6) distorted.

Secondly, it is symmetrical, giving a central role to the ‘con-

cept’ as intermediary between the ‘symbol’ and the ‘object’.

Thirdly, the dashed line indicates a weaker link than the

solid lines. It displays very neatly the diagrammatic features

that are unavailable in classic symbolic forms. However, it is

also clear that these features can be abused, thus conveying

the wrong information. Consider an alternative diagram

that is topologically equivalent but sends a distorted mess-

age. As a consequence, Fig. 1 B is more difficult to read and

understand. That diagrams can have a syntax is self-evident;

that this syntax has a clear-cut semantic counterpart is less

evident. This, we believe, hints at something that most ac-

counts of semantic networks ignore. If, as most people be-

lieve, a semantic network is a better way to represent knowl-

edge to a reader (whether human or machine) then where,

in fact, does the improvement lie, and are there any limits to

this improvement? Furthermore, are there clear-cut advan-

tages when it comes to machine representations, or is it just

‘all the same’ when knowledge gets digested by the machine?

Levels of representation

Brachman14 examined five levels of representation that se-

mantic network designers commonly used, and assumed


Box 2. Meaning

The meaning triangle shows that meaning is a composition of two relations, from the symbols of language to the real world, through

a concept-forming agent. However, this is not the only use of the term meaning. In formal semantics, symbolic structures are given

meaning through interpretative mappings, or models, where the target of the mapping is some formal construct that uses math-

ematical (or at least abstract) ideas. The formal construct is often meant to capture the nature of the teal world in some way but it

is not, in fact, the real world. Typically, this is done for symbolic systems such as formal logk?’ and for programming languages’~f.

Brachman’s levels of representationg Linguistic: arbitrary concepts, words and expressions.

Conceptual: semantics ot conceptual relations (cases), primitive objects and actions.

‘Epistemological’ (structural): concept types, conceptual subpieces, inheritance and structuring relations.

Log&: prepositions, predicates and logical operators.

Implementational: atoms and pointers.

a McDermott, D. and Doyle, J. (1980) Non-monotonic logic I Artif. Intel/. 13, 41-72

b Poole, D.L. (1988) A logical framework for default reasoning Artif. Intel/. 36, 2748

c Przymusinski, T. (1991) Three-valued non-monotonic formalisms and semantics of logic programs Artif. Intel/. 49, 309-343

d Epstein. R.L. (1994) The Semantic Foundations of Logic: Predicate Logic, Oxford University Press

e Strachey. C. (1966) Towards a formal semantics, in Formal Language Description Languages (Steele. T.B., ed.), pp. 198-220. North Holland

f Schmidt, D.A. (1986) Denotational Semantics: a Methodology for Language Development, William C. Brown

g Brachman, R.J. (1979) On the epistemological status of semantic networks, in Associative Networks: Representation and Use of Knowledge by

Computers (Findler. N.. ed.), pp. 3-50. Academic Press

that they were all equivalent, in the sense of having the same

ultimate meaning (Box 2).

Clearly, the intention was to show that representations

can be ‘high-level’ or ‘low-level’, just like computer languages.

In this sense, then, logic is the assembler of representation

systems, being only one step above the machine level, whereas

a natural language is the furthest away from the machine.

Thus, a semantic network represented at the conceptual

level can be translated into the epistemological level, then to

the logical level, and finally to machine data structures.

Whereas we could agree with the intent, it is difftcult to

see how it fits with the reality of how knowledge represen-

tation is typically carried out and, more importantly for us,

with the meaning triangle. We think that it is more realistic

to make a simple split, as Sowa does, between external rep-

resentations and internal representations, because there are

important differences between human-readable forms and

forms that can be stored and processed in a computer’s

memory.

The meaning triangle does not distinguish among levels

of symbol; in some sense, all symbolic representations,

whether using linear text, such as logic, or two-dimensional

forms such as semantics networks are equivalent. What is

more, both logic and semantic networks (at least Sowa’s

conceptual graphs) have equivalent forms in the other

mode. Conceptual graphs have a linear, textual form, and

logic has a diagrammatic form - Peirce’s existential graphs.

(Although Sowa himself sees his conceptual graphs as

distinct from other semantic networks, they are clearly in

the same family of representation systems, having all the

features described in Box 1.) Figure 2A-D shows Sowa’s ex-

ample from Ref. 1 in all four forms. The sentence (another

linear form) is: ‘If a farmer owns a donkey, then he beats it’.

Other notations can be seen in Refs I5 and 16.

If we attempt to reconcile these forms in the context of

the meaning triangle, we get Fig. 3A for the two systems of

conceptual graphs (CGs) and first order logic (FOL), each

with their two symbolic forms.

Thus the symbol systems all have the same meaning,

although the method of mapping through the concept node

is different in each case, hinting at a similar split in the

concept node.

The representations within the circle in Fig. 3A are

alternate external forms. We can add the distinction be-

tween external and internal representations to this picture.

A computer program is a symbol system, just like FOL or

CG notation, but before adding these internal represen-

tations to the meaning triangle, we believe it is instructive

to add a level between machine level and all the external

symbolic forms. A programming language is used as an

intermediary between concept and machine. The object-

oriented languages support abstractions corresponding to

Brachman’s epistemological level, i.e. types, inheritance,

containment, etc. (see Ref. 17 for an excellent exposition

of these ideas). What one does in designing a program is to

use these abstractions to support perceived relations and

structures in the real world. However, programming these

relations and structures directly is too difficult, so we need

to use a programming language to implement abstract

data types (ADTs). The better languages (object-oriented

ones) support our ADTs directly and the compiler can

then translate these into machine form. It is natural to use

a pointer/node abstraction to support a diagrammatic

semantic network, but is this the most appropriate form

for linear expressions, or is there even a better way to

form these abstractions, as in Lendaris’s hash-tables (see

Box 3)? Clearly, we have choices, governed by consider-

ations of efftciency, size, speed, etc. (the usual computer

science concerns). Thus, the internal representations must

be mediated, through necessity, by an ADT, one which is

supported by our chosen language. Figure 3B shows these

links.



L

C D

(PTNT)->[DONKEY:*~], (STAT)+[FARMER:*f]]

-[[BEGAT]- (AGNT)->[T:*f], (PTNT)->[T:*d]]]

Fig. 2 Symbolic representations of the s@ntence ‘If a farmer owns a donkey, then he beats it’. (A) a diagrammatic, logical form, (B) a conceptual graph form, K) A linear, logical form, (D) a linear conceptual graph form.

How do computers change things?

We have discussed the meaning triangle which, as we have

seen, really relates an ontology of the world with a con-

ceptual schema and an (external) symbol system. However,

through the field of artificial intelligence, the computer

scientists have added a fourth leg - the internal represen-

tation. The advantages of using these internal representations

are:

(1) They can make the linguistic representations com-

putable. This means that the rules of a formal system can

be applied to the representations to explore complex possi-

bilities of inference and even proof. Without the computer,

logic would be too hard for describing complex systems. In

fact, Prolog, which uses a restricted but useful form of first

order logic, can make logic accessible even to people un-

versed in the arcana of the formal calculus.

(2) They make persistent databases of knowledge poss-

ible. We can store, in a permanent fashion, effkient repre-

sentations of human memory that permit complex queries

and deterministic processing. Again, without them, these

applications would not be possible.

(3) They can make diagrammatic representations useful

over and above their use in small motivational examples.

Human-computer interface technology helps here. We be-

lieve that this particular aspect of visualization has been ne-

glected when it comes to discussing semantic networks.

A

concept

object --- - _ _ _ _ _ _ _ _

B concept

machine Fig. 3 Extensions to the meaning triangle. (A) The meaning triangle can incorporate different forms of symbolic representation, (B) addition of the Abstract Data Type (ADT) level.

Trends in Cognitive Sciences - Vol. 1, No. 5, August 1997


Box 3. Implementing semantic networks

just like the arrows in a diagrammatic form. Prolog’s internals

Brachman’s lowest level has the machine concepts of ‘atoms

contain similar hidden pointers. However, other strnctures can

and pointers’, betraying his preference for implementations

be used. Lendaris uses a hash-table form to represent graphs’

built using Lisp. Any symbol system can be implemented using

and object-based implementations are also available. There is

any data structure with adequate features. Traditional imple- menration of systems based on logic have been in Lisp because Lisp’s list structnres are perfect for representing linear expressions. What is mote, pointers (not visible in the source code but present in the programming model) can provide co-references,

Structures, Boston, August 1990

nothing special about atoms and pointers other than ease of im-

b Barnden, J.A. (1995) Semantic networks, in The Handbook of Brain

plementation. Semantic networks can also be implemented as

Theory and Neural Networks (Arbib, M.A.. ed.), Bradford

neural networks and there are interesting similarities and differ-

Books/MIT Press

ences between the twob.

References a Lendaris, G.G. (1990) Conceptual graphs as a vehicle for improved

generalization in a neural network pattern recognition task.

Proceedings of the Fifth Annual Workshop on Conceptual

How useful diagrams can be and what their limitations are

needs thorough investigation.

All of these uses rely on efficient internal represen-

tations, fit only for machine consumption. The computer

must be programmed, however, to cater for the limitations

of the human mind. Two areas of concern are information

retrieval and presentation. If too much information is

thrown at the user too fast, the mind can easily be over-

loaded. A database can be programmed to dump itself on

the screen, but there is little point, if no one can read it. The

manner in which information is presented is also crucial.

Only certain modes yield good results. A visual display that

is too complex also produces cognitive fatigue. These issues

are acknowledged, but there are no easy solutions, and old-

wives’ tales are common. For instance, ‘a picture is worth a

thousand words’ is true but it does depend on what kind of

picture, and how it is presented. Figure 4 shows a complex

semantic network in a typical problem-solving system. We

defy anyone to ‘get the picture’ from this graph without a

lot of help in terms of descriptive support.

The question of whether semantic networks convey

computational advantages over other representational ap-

proaches such as logic is somewhat vexed. The visual advan-

tage (if any) of semantic networks for human beings rests

mainly on the property of locality. As Sowa points out’, for

a given real-world item, such as the person John, that is

SENSOR-CONFIG]

FLOW-DIR: OUT FLOW-DIR: OUT

Fig. 4 A complex semantic network.



A B

(PRST)-~[[CflT}I<-(RGNT)~-[CHRSEl->(PTNT)-~lMOUSE~~

Fig. 5 Conceptual graphs and linear forms can be remarkably similar. (A) Conceptual graph, (B) linear form of (A).

i

represented in the network by a node, all information that

is specifically about John is attached to the node. And why

should the attachment of the John node be important? It

is because the human eye can follow the links easily and

efficiently.

Now, the typical computer implementation methods

for semantic networks preserve this conception of locality

to some important degree, converting it into a type of com-

putational locality. In fact, there is a virtually universal tacit

assumption on the part of the semantic networks com-

munity that an implementation does preserve the locality.

A neutral and general way to put the assumption is as

follows: when the system is attending to a given node N, it

is quick for the system to transfer attention to the node or

nodes that are connected to N by a single link of a given

We. For instance, if the system is attending to the ‘WATER

node in Fig. 4 then it is quick for it to transfer attention to

the ‘BODY node, through the ‘CONT’ (‘ains’) link.

Much as the human eye can follow links efficiently in a

diagram of the net, the computer is assumed to be able to do

so in its internal implementation of the net. Thus the visual

benefit is transformed (partially) into a computational benefit.

All this provides a contrast with formal logic, which has

no comparable implemenmtional assumptions, tacit or other-

wise. But we must resist the temptation to say that, there-

fore, semantic networks have a computational advantage.

In practice, when we implement a logical representation

scheme in a computer, for reasons of efficiency we need to

include indexing schemes in the implementation. This is to

allow related pieces of information to be brought together

efficiently. For example, one indexing scheme is for each in-

dividual constant symbol in the logic, such as the constant

‘John’, to be an efficient indexing key to all formulae that

involve that symbol. Thus, information that is specifically

about the person ‘John’ should be quickly accessible from

the ‘John’ constant symbol (see Ref. 18 for a useful text-

‘zt r-J-0

t 1 u-u OS’

Fig. 6 Examples of concept maps.

book introduction to indexing in logic implementations).

Therefore, in implementing logic, we consciously impose a

parallel to the computational locality that is assumed to

hold for semantic networks.

A full comparison of logic and semantic networks would

need to address the way in which semantic networks facilitate

inheritance-based reasoning and allow for exceptions to in-

herited information. Again, there is no real advantage over

logic, because logics can be implemented or designed in such

a way to have those advantages. One specific recent movement

is to use description logics’8, which are designed specifically

to capture some advantages of semantic networks.

Visualization in concept maps

Often, diagrams have been advocated as useful brainstorm-

ing tools (for example, Ref. 19). Conceptual mapping is a

recognized way of getting ideas down on paper so that their

relationships can be seen rather than contemplated ab-

stractly. In this sense, the properties of a graphical network

become important. Placement of the nodes on the page is

important; the more centrally placed a node is, the more

‘central’ the associated concept is in the cognitive structure.

More importantly, the length of the links signifies similarity

in concept. This property has been exploited in Pathfinder

networkP, where a clever algorithm determines the best

placement of nodes on the basis of their cognitive ‘distance’,

as reported by a human agent. As mentioned above, this as-

pect of the diagrammatic form is absent from many de-

scriptions of semantic networks, as if it was somehow irrel-

evant. At least Sowa attempts to say why the diagram is

better than the corresponding FOL form’, but then pro-

ceeds to give a linear form in his conceptual graph notation

that is clearly as good as the diagram! The two are virtually

interchangeable (Fig. 5A and B). Perhaps the example is a

poor one, since there must be advantages to the diagram-

matic form, it is just that no one can quite put their finger

on it. How nearness and connectedness figure into the situ-

ation is never spelt out.

A concept map is a visualization technique used for

brainstorming, for exploration of ideas and for discussing

complex systems among the members of a group. Excellent

examples of different types of concept maps, and their uses

can be found within Ref. 21. Figure 6 shows a ‘systems’

concept map from Ref. 2 1, which is meant to be similar to

a flowchart. However, there is no discussion about why the

diagrams look like they do, or exactly how they convey the

required information. A concept map for water can be

found within Ref. 22 that looks suspiciously like a semantic

network. Indeed, the only real difference between the two,

as presented there, seems to be a pragmatic one, and per-

h aps more formality in the semantic networks choice of re-

lations. Concept maps are used for knowledge acquisition

and analysis, and have a useful heuristic nature, whereas, in

Trends in Cognitive Sciences - Vol. 1, No. 5. August 1997

Fig. 7 For all forms, usefulness declines es complexity increases.

Outstanding questions

l How can semantic networks best represent procedural knowledge? l Do semantic networks have any measurable benefits for knowledge

acquisition over text-based methods? l Does the visual aspect of semantic networks help or hinder knowledge

representation? l What is the best way to implement a knowledge representation system

based on semantic networks? l Can (and should) the ties to formal logic and logical inferencing be

broken to make semantic networks truly distinctive? l At what level of complexity do the visual aspects of semantic networks

break down?

and they have much to offer the field of concept mapping in

terms of rheir formal underpinning and the experience of

the artificial intelligence community that uses them.

general, semantic networks are used for representation in

systems, especially computer systems.

It is clear from all the examples of concept maps that

their usefulness degrades as the size increases. As Fig. 4

shows, these diagrammatic forms become useless as com-

plexity gets too high, and the linear form can serve just as

well (or as badly, since the linear form is always difficult to

read). A graph such as that shown in Fig. 7 demonstrates

the relative usefulness of the alternate external represen-

tations as complexity increases. Diagrams are better than

the linear form for reasonably sized chunks of knowledge,

but ultimately rhey both fail because of the limitations of

human cognition.

Conclusions

Semantic networks possess several advantages as a rool for

knowledge representation. They are easier to read than lin-

ear forms (although this advantage is nullified by too much

complexity). They also lend themselves to more natural im-

plementations than algebraic or expression-based systems

(although these techniques are not confined to semantic

networks, and may not be the most efficient methods).

Furthermore, they suggest special forms of inference based

on link-following (although these forms of inference can

also be implemented in other systems).

We have not mentioned an important aspect of semantic

networks that deserves a longer discussion than is possible

here. The representation 6f procedural knowledge (know-

ing ‘how’, instead of knowing ‘that’) has been approached

from several different angles but has nor yet yielded, we

believe, to a definitive analysis. Bringing together the ideas

of procedural attachment (linking concept boxes to arbitrary

procedures to support inference), the inference engine that

interprets a network representation and formal semantics of

programming languages might be a starting point.

Semantic networks have led a dual existence, as a moti-

vational diagrammatic form of knowledge representation

and as an internal, computerized form, suitable for a variety

of computarional methods. However, although their visual

aspect has been acknowledged, it has not been studied

extensively enough. Their resurrection as tools for visual-

ization of the structure of knowledge should be explored,

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Semantic networks: visualizations of knowledge

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