MEANING AND ONTLOGICAL COMMITMENT: A SURVEY OF THE
USE OF THE TERM ‘SEMANTIC PRIMITIVE’
KHURSHID AHMAD
Department of Computer Science,
Trinity College,
Dublin, IRELAND.
THIS DOCUMENT IS A PRE-FINAL DRAFT OF A PAPER BY THE AUTHOR. PLEASE USE
NORMAL CITATION CONVENTIONS WHILST REFERRING THE CONTENTS OF THIS PAPER
0.
INTRODUCTION ........................................................................................................................................ 4
1.
STUDY OF MEANING AND THE TERMINOLOGY OF SEMANTICS ............................................. 7
2.
‘PHILOSOPHY’ AND SEMANTIC PRIMITIVES................................................................................ 10
2.1
2.2
2.3
2.3
2.4
3.
LANGUAGE AND SEMANTIC PRIMITIVES ...................................................................................... 15
3.1
3.2
3.3
3.4
3.5
4.
SEMANTIC FEATURES ....................................................................................................................... 15
SEMANTIC RELATIONS FOR GENERAL PURPOSE LANGUAGE ...................................................... 18
SEMANTIC RELATIONS FOR SPECIAL PURPOSE LANGUAGE ........................................................ 22
X-BAR SEMANTICS............................................................................................................................ 23
ISO STANDARDS ............................................................................................................................... 27
PSYCHOLOGY AND ‘SEMANTIC PRIMITIVES’ .............................................................................. 30
4.1
4.2
4.3
5.
THREE 'THEORIES OF MEANING'...................................................................................................... 10
'ORDINARY LANGUAGE' PHILOSOPHY ............................................................................................ 11
'CONCEPTUAL ANALYSIS' IN PHILOSOPHY .................................................................................... 12
POPPERIAN PERSPECTIVE ................................................................................................................ 13
LOGICAL SEMANTICS ....................................................................................................................... 14
CATEGORISATION AND CLASSIFICATION ...................................................................................... 30
PSYCHOLOGICAL 'REALITY' OF LEXICAL SEMANTIC RELATIONS ................................................ 32
CHAFFIN AND HERMANN ................................................................................................................. 34
ARTIFICIAL INTELLIGENCE: KNOWLEDGE AND SEMANTICS ............................................... 35
5.1
NETWORKS AND 'MEANING' REPRESENTATION ............................................................................ 35
5.2
RELATIONAL GRAPHS AND SEMANTIC 'PRIMITIVES'.................................................................... 36
5.2.1
Conceptual Graphs ........................................................................................................................ 39
5.2.2
Conceptual Dependency Grammar................................................................................................ 43
5.3
'KNOWLEDGE LINES', 'NEMES' AND 'NOMES'.................................................................................. 45
6.
CONCLUSIONS......................................................................................................................................... 49
3
0.
Introduction
The exchange of information between individuals, especially in the context of a well-defined
community like scientists, sports-enthusiasts, followers of a religion/cult, usually depends on
two factors, amongst many: First, the individuals use language-forms in a given special
language of a general language to convey information about concepts and artefacts of their
specialism. Second, the individuals use and attempt to understand signs and symbols that are
used for encoding information within their specialism and attempt to decipher what these
signs and symbols represent. The first factor relates to semantics as the term is used amongst
the linguists and the second factor relates to the term as used by the logicians. The exchange
of specialist information is usually attempted in a subset of a natural language where there is
a premium in the succinct and unambiguous use of terms and the words that inter-relate the
terms of a domain.
Semantics, the premier science of the study of meaning in natural language (linguistic
semantics) study of meaning can be encapsulated in artificial languages, like logic and other
symbol systems, is in itself an ambiguous term. This means that people in different
disciplines maybe referring to the same concepts and artefacts but would be calling them by
different names or they would be confuse everybody else by calling different concepts or
artefacts by the same name. This ambiguity also confuses the students of semantics in the
various cognate disciplines of philosophy, psychology, linguistics and artificial intelligence,
say, where the same keyword is used for expressing meaning relationships between concepts,
between artefacts and between concepts and artefacts. This meta-level study of meaning in
the above mentioned cognate disciplines may lead people in the different disciplines may
either call the same (or similar) (relational) concepts by different terms or call the different
concepts by the same name. In order to investigate the ambiguities it is important to look at
the representational primitives used in the four cognate disciplines mentioned above. What
hope to look at, with the help of the reader of this book, is to document different names for
semantic features and semantic relation in these disciplines.
Consider some of the frequently encountered ‘popular’ semantic features and semantic
relations between linguistic units, like words (or terms) and sentences, in the linguistics
literature (respectively Tables 1a and 1b below). The intention here is to elaborate on the
intuitively plausible but terminologically complex notions of semantic features and semantic
relations:
4
Table 1a: Semantic features and relations between Words, Terms or Phrases
Descriptor
Meaningful/
Meaningless
Lexical Ambiguity
Anomaly
Descriptor
Synonymy
Antonymy
Homophony
Inclusion
Overlap
Field Relations
Example
Semantic Features
bachelor/
abracadabra
a bat
strawberry truth
dream diagonally
colourless green ideas
Semantic Relations
Example
automobile:car; kid:child
hot ≠ cold
small≠medium≠large
bank (n. and v.)
sister (female)
Father; Uncle; stallion (Male)
Say; whisper; shout (vocalisation)
Colour terms;
Kinship terms;
Cooking terms;
Nautical terms;
Language for Special (or Specific)
Purpose terms.
5
Table 1b: Semantic features and relations between Sentences or Phrases
Descriptor
Example
Semantic Features
Meaningful/
He came to the AI class at 9.
Meaningless
Colourful green ideas sleep
furiously
Ambiguity
She visited a little girl's school
Communicative potential
Declarative sentences;
Imperative sentences;
Interrogative sentences;
Truth Properties
Linguistically true/false sentences;
Empirically true/false sentences.
Semantic Relations
Descriptor
Example
Truth Relations: Entailment:
The car is red entails the car has a
colour
Truth Relations: Presupposition:
The present King of France is bald.
The present King of France is not
bald.
There is a present King of France
Logical Form and
Analytic sentences
6
1.
Study of Meaning and the Terminology of Semantics
The term ‘semantics’ has at least three, interdependent and interactive, ‘meanings’ (Crystal
1991:310-11):
Linguistic semantics concerns the study of meaning in language. Linguists interested in
language production and comprehension study ‘meaning’ relations, like synonymy and
antonymy, or they analyse sentences in terms of a semantic representation. Another
approach to the study of meaning is to focus on the semantic features of lexical items,
such as animacy, and spectral features like number, state, and so on. Linguists interested
in language acquisition tend to analyse and predict the growth of lexical items through the
use of semantic features and the complexity of these features.
Philosophical semantics encompasses the study of relations between linguistic
expressions and the phenomenon in the world to which they refer, and considers the
conditions under which such expressions can be said to be true or false, as well as the
factors which affect the interpretation of language as it is used.
Logical semantics focuses on the study of the meaning of expressions in terms of logical
systems of analysis or logical calculus.
7
Linguistic, philosophical and logical semantics have their own terminologies and there is
extensive overlap and borrowing between these terminologies. Indeed, the study of meaning,
in language, philosophy or logic, generates a large number of neologisms which can be
(slightly) confusing for the reader. Reviewing Cruse’s Lexical Semantics, Aitchison notes
‘what some people might regard as flaws in the book’: ‘The most obvious problem involves
the terminology: new coinages abound, such as xenonymy, philonymy, meronymy, paronymy,
plesionymy. These overload the text with technical terms, and make it tiresome to read. In
addition, they are not clearly signalled as new, but are interleaved with terms already in use,
such as complementarity, hyponymy. [....]. A further problem is that a number of wellknown words, such as entailment, paradox, are used with a non-standard meaning.’
(Aitchison1990:1148).
Scholars in all branches of knowledge continue to reinterpret the scope of their respective
domains during the so-called ‘normal or evolutionary period’ of the development of a given
branch (cf. Thomas Kuhn 1976). Semanticists are no exception. According to Laudsaw, ‘the
term semantics covers a wide range of issues involving meaning, significance, interpretation,
and understanding of language’ (1990:89). Laudsaw’s survey of the state-of-the-art in
semantics focuses primarily on the semantics of sentences, on compositionality constraints,
on the mapping of syntactic structure to semantic representation, and on the semanticists’
inputs to the ‘mind/brain’ debate in epistemology and philosophy and psychology. In the
broadest sense of the term, this debate is about whether representations of objects, events,
feelings etc., stand directly for aspects of human experience (about the ‘external’ or the
‘perceived’ external world) or whether the representations are primarily ‘mental objects’.
The ‘mental objects’ owe their existence to the human cognitive faculties. The term
‘cognitive semantics’ (Lakoff 1987), Lakoff elaborates on his neologism ‘cognitive
semantics’ to :
Cognitive Semantics attempts to develop a semantic theory based on the notions of
‘meaningful concepts’, ‘cognitive models’, and philosophical issues involving ‘meaning,
understanding, truth, reason, knowledge, and objectivity’. Extensive use is made of
constructs like ‘image schemas’ and interrelationships used to encode and decode
knowledge and experience, like ‘propositions’, ‘metaphor’, ‘metonymy’, and, ‘symbolic’
representation and reasoning.
8
Jackendoff has discussed the role of ‘cognition and semantics’ in (linguistic) semantics
literature to incorporate psychological aspects and the artefacts of these aspects in the study
of ‘meaning, significance, interpretation, and understanding of language’ (A popular expose
of this genre can be found in Aitchison's entertaining account entitled ‘Words in the Mind’
(1987)). Jackendoff introduces yet another neologism, ‘conceptual structures’, to elaborate
on the ‘interdependence’ of ‘cognition and semantics’ (1983): ‘Conceptual structures’ are
mental objects which are the cornerstone of Jackendoff’s theory. This theory is constrained
by ‘grammatical’ and ‘cognitive’ constraints. ‘The grammatical constraint says that, other
things being equal, a semantic theory which explains otherwise arbitrary generalisations
about the syntax or the lexicon is to be preferred. The cognitive constraint takes the form of
a hypothesis that conceptual structures are the representations used by the sensory and motor
systems as well as the linguistic system.’ (Laudsaw 1990:107).
More recently, Jackendoff has introduced one more neologism, namely ‘conceptual
semantics’: ‘Much of my research over the past fifteen years has been concerned with laying
the foundations of a theory of meaning called Conceptual Semantics, using first principles
parallel to those that motivate generative syntax and phonology’ (1990:2). Jackendoff has
been concerned with the comments and criticisms of Jerry Fodor (1987) on the ‘neocartesian’
approach of Noam Chomsky. The description of ‘conceptual semantics’ is, on the one hand,
peppered with attempts to show how Jackendoff’s ‘conceptual structures’ is ‘on the face it’
closer to Fodor’s position (cf. ‘“Mentalese”- in effect I-semantics’ Jackendoff 1990:13). On
the other hand, however, Jackendoff remarks that his work differs from Fodor’s in that
‘Fodor insists that all combinatorial properties of I-concepts must be mirrored in Reality,
while a theory of pure I-semantics is not necessarily subject to that constraint’. Jackendoff
concludes that ‘Fodor’s insistence on Intentional Realism is misguided for the purpose of
doing scientific psychology’ (1990:15):
Conceptual Semantics is concerned most directly with the form of the internal mental
representations that constitute conceptual structure and with the formal relations between
this level and other levels of representation. Jackendoff’s ‘proposition’ is that ‘the innate
formation rules include a repertoire of major conceptual categories, the “semantic parts of
speech.” These categories include such entities as Thing (or Object), Event, State, Action,
Place, Path, Property, and Amount.’ (Jackendoff 1990:43).
The above outline of various neologisms indicates that semanticists, particularly those with a
linguistic or ‘language science’ background are increasingly, and rightly, concerned with
aspects of meaning which have been discussed by philosophers and psychologists. The
linguists may convincingly claim that their ‘viewpoint’ is different from the latter two.
9
Nevertheless, it is important at this stage to seek some terminological clarification,
particularly with regard to the so-called ‘semantic primitives’ in philosophy and in the
description of languages.
2.
‘Philosophy’ and Semantic Primitives
We believe that an adequate semantic description of natural languages must record facts of
meaning, linguistic reference, and truth conditions. Cognitive scientists, including AI
researchers, borrow freely from three traditions.
2.1
Three 'theories of meaning'
In the literature on philosophical semantics one encounters three major theories of meaning:
(i) The Referential Theory of Meaning: The meaning of each expression E is the actual
object it refers to, its referent. There are a number of advantages associated with this theory
as well some problems. For instance, consider the problems associated with the oft-quoted
sentence
Each of the above consequences is false
It follows that:
(1) If an expression E has a meaning, then it must have a referent
(2) If two expressions have the same referent, then they have the same meaning
(3) Anything that is true of the referent of an expression is true of the meaning.
(ii) Mentalist Theories of Meaning: The meaning of each expression E is an idea (or
ideas), I, associated with E in the minds of speakers. The problems associated with this
theory are that:
(1) The notion of an idea is too vague to allow the theory to predict anything specific
and thus the theory is not testable
(2) If the notion of an idea is made precise enough then the theory turns out to make
false predictions.
(iii) The ‘Use Theory’ of Meaning: The meaning of each expression E is determined by its
use in the language community (developed by Ludwig Wittgenstein in his second phase).
The problem here is that the relevant conception of use must be made precise.
10
Aristotle’s work on philosophical semantics led him to describe a ‘hierarchy’ of ten general
superordinates - the primitives which Aristotle used to examine language signs/linguistic
expressions and real-world phenomena:
Substance, Quantity, Quality, Relation, Place, Time, Position, Place, Activity and Passivity.
2.2
'Ordinary Language' Philosophy
Wittgenstein contributed to the notion of ordinary language philosophy: a commitment to the
adequacy of already existing language and a need for attending more carefully to how this
language is actually used. Wittgenstein argues that philosophical problems arise when
language ‘goes on holiday’, that is, when we misuse language. The situation comes not in
answering the problems philosophers pose, but by dissolving the philosophical problems by
appealing to have we ordinarily use the language.
Austin, Searle and Grice concur with Wittgenstein that rather than trying to reform ordinary
language, philosophers should attend more carefully to how it functions. However, they differ
from Wittgenstein in that they emphasise the use of language as a kind of action and analyse it
accordingly. In the following we outline a methodology, attributed to Austin, as to how
language can be used for discussing philosophical problems (Williams 1988:27-30).
First, collect the vocabulary and ideas to talk about a particular domain, like
responsibility, and then examine in detail the nuances involved in the use of the terms and
idioms. Austin recommended such techniques as free association, reading of relevant
documents and examination of dictionaries.
Second, construct statements that might actually be used in normal speech and ways of
saying things which would be preferred to others. This activity has to be carried out prior
to any philosophical theorising, since such theorising could contaminate the evidence and
destroy sensitivity to how people actually use the language. The point of the exercise is to
uncover the subtle distinctions made in the language that may be of use when one begins to
construct philosophical theories.
Third, formalise philosophical theories that both account for how the terms and idioms of
the language are normally used and draw upon the insights about ordinary usage
discovered in the earlier steps.
Austin's 'methodology' can be viewed as a three step exercise: elicitation of basic vocabulary
and idioms and their analysis; observation of how the elicited items are represented in
language, or the observation of how these items can be represented, particularly as in
sentences; and, how
the vocabulary and the sentences can be used for reasoning and/or the dissemination of
knowledge through inference. The following Table summarises Austin's methodology:
11
Data
Techniques:
Data:
Techniques:
Data:
Techniques:
STEP I: Elicitation
Vocabulary and idioms
Free association; reading of relevant document;
examine dictionaries
STEP II: Representation
Statements that might actually be used in the language
Construct the statements; examine terms used in
normal speech; identify ways of saying things
preferred to others
STEP III: Reasoning/Dissemination
Theories that both account for how the terms and
idioms of the language are normally used and draw
upon the insights about ordinary usage discovered in I
and II
Inference; Axiom elicitation
2.3
'Conceptual Analysis' in Philosophy
Philosophers 'have been doing conceptual analysis ever since Socrates taught Plato how to
analyse Justice' (Sowa 1986: 294). Sloman amplifies this point by noting that there may be
three potential uses of conceptual analysis in Philosophy (1978: 84):
(a)
'the main function of conceptual analysis is to help clarify or resolve philosophical
problems’;
(b)
'occasionally also to provide a basis for criticising some uses of language';
(c)
conceptual analysis 'has another important purpose, namely to find out things
about people and the world'.
The last two uses of conceptual analysis, that for criticising some uses of language and to find
out more about people etc., are indeed activities which are not dissimilar to those of a
linguistic semanticist.
The discussion regarding the use of conceptual analysis in philosophy will focus mainly on
Sloman's 'The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind':
this text, in our view, provides some insight into how 'developments in computing can lead to
major advances in the scientific studies of man and society' and the relevance of this
development to philosophy (1978: 3). Sloman also deals with 'some of the connections
between Philosophy and recent steps towards the design of a mind' (1978: 242).
We will now present a brief overview of a strategy for analysing concepts proposed by Sloman
(1978: 84-102) and show how relevant this strategy may be to terminology and possibly AI.
Sloman has presented his strategy, in characteristic self-effacing style, as essentially a list of
techniques for ‘collecting “reminders” about how our concepts work’.
12
• the acquisition of concepts;
• the representation, or more precisely, organisation of concepts;
• the deployment or potential application of the analysis for solving philosophical
problems.
Sowa has also discussed Sloman's strategy (1986: 297-8) and has reduced Sloman's checklist
of 15 major points (described below) to a five-point checklist which is similar to Sloman’s
'but the terminology is adapted to conceptual graphs' (1986: 287). Sowa's discussion includes
(a) the 'instances' and 'definition' of concepts; (b) interrelationships between concepts
expressed through 'type hierarchies'; (c) representation of concepts using 'schemata'; and (d)
deployment of the results of the analysis through the use of canonical graphs. Sowa's
categorisation also relates to the acquisition, representation, deployment and 'explication' view
expressed above.
CONCEPTUAL ANALYSIS DUE TO SLOMAN
ACQUISITION
• Collect description of various instances of a given concept
• Try criticising and extending the definitions given in dictionaries
• Try to collect list of related words and phrases to refine the understanding of the
concept(s) under analysis. (Use Roget's Thesaurus or dictionaries).
• Try to collect lists grouped in different ways
REPRESENTATION
• Try fitting very general categories (VGC) (e.g. event, act, state process....) to the lists of
related concepts
• For each concept being investigated ask whether it refers to a specific kind of thing
(VGC) or covers a whole lot of different things (polymorphous)
• For polymorphous concepts, ask whether there are 'central' and some 'peripheral' or
'derivative' cases
DEPLOYMENT
• What sort of things can be explained by instances of the concept?
• 'Causality' related to concepts
• Find a set of relatively 'primitive' concepts and relations
• Try to test the theories so developed using computers to simulate concepts
• What is the effect of the requirement of prerequisite knowledge or ability?
EXPLICATION
• How concepts are learnt
• How to test the validity of statements using concepts
• Investigate concepts using imaginary stories
2.3
Popperian Perspective
An alternative view of concepts is presented by Popper. He has discussed the role of
concepts or terms in the context of Platonic theories (1979:120-124) and in the context of
"realism in logic" (1979:304-318). Popper, as is well known, is a strong proponent of "the
problem of theories" which in his view should be the real point of philosophical
investigation. Popper argues that the school which focuses on "the problems of universals",
that is those people who subscribe to the Platonic view of the world, should really
13
concentrate on problems related to "truth; argument". Popper suggests that "concepts, and
the associated conceptual systems (and problems of their meaning, or the meaning of words)
are not comparable in importance with theories, and their associated theoretical systems and
problems of their truth, or truth of statements" (1979:123). Popper argues that "concepts are
partly means of formulating theories, partly means of summing up theories. In any case their
significance is mainly instrumental" (1979:123-24). Also Wüster's use of the term concept is
very close to that of Plato's and, as mentioned above, Wüster regards concepts as "mental
elements", which form the basis of a number of current terminology data banks and
glossaries, whereas Popper argues that "one should never get involved in verbal questions or
questions of meaning, and never get interested in words" and "one should never quarrel about
words, and never get involved in questions of terminology" (1979:310).
In the following table we compare two frameworks, that of Plato and of Popper, which may
form the discussion of what a principled framework for terminology should be. Note that
both frameworks label ideas as 'concepts (or designation or terms)' or as 'theories (or
statements or propositions)' which could be represented using 'words' or 'assertions'
respectively in a Platonic and Popperian framework. Other 'properties' of 'Ideas' are also
shown:
Some properties of "ideas" according to Popper (1979:124).
IDEAS
Properties
Framework
Platonic
Label
CONCEPTS
DESIGNATIONS or TERMS
Representation Primitives
Semantics
Elaboration through
Elaboration Primitives
WORDS
MEANINGFUL/ABSURD
DEFINITIONS
UNDEFINED CONCEPTS
Popper
THEORIES
STATEMENTS or
PROPOSITIONS
ASSERTIONS
TRUE/FALSE
DERIVATIONS
PRIMITIVES
PROPOSITIONS
2.4
Logical Semantics
According to Tarski 'Semantics is a discipline which, loosely speaking, deals with certain
relations between expressions of a language and the objects (or "states of affairs") referred
to" by those expressions. As typical examples of semantic concepts we may mention the
concepts of designation, satisfaction, and definition as these occur in the following
examples:
14
[designation:] the expression "the father of his country "
designates (denotes) George Washington;
[satisfaction:] snow satisfies the sentential function (the condition)
"x is white";
[definition:]the equation "2.x = 1" defines (uniquely determines)
the number 1/2' Tarski (1944:345/1952:13).
3.
Language and Semantic Primitives
Major texts in semantics use a variety of terms to describe the various approaches to studying
meaning in language; sometimes these terms are new coinages, and in some cases they are
idiosyncratic, which may impede clarity. Often their use of terminology reflects the
particular approach they are adopting to the study of meaning. Our view of semantic
primitives comprises the ‘irreducible’ or ‘basic’ semantic features of linguistic units, i.e.
words, terms and sentences, and semantic relations which inter-relate linguistic units.
Semanticists define their own primitives: sometimes these primitives can be found in the
work of other semanticists and sometimes there is a ‘violent’ disagreement about the primacy
of a primitive. We now present a brief survey of the primitives used to describe semantic
features and semantic relations in the literature.
3.1
Semantic Features
Margaret Masterman was among the early workers involved in the development of a
computer-based ‘semantic dictionary’ using so-called ‘semantic nets’. According to Sowa
(1989:14), Masterman used 100 primitives, such as:
folk, stuff, change, go, talk,
to create a 15,000 word dictionary. More recently, McArthur (1981) in the ‘Longman
Lexicon of Contemporary English’ used 14 superordinate categories to produce a 784 page
semantic lexicon. These categories are not as ‘crisp’ as those of Aristotle, but rather they are
characterised by an almost Wittgensteinian ‘fuzziness’ of categorical ordeals:
Life and Living Things; The Body: Its Functions and Welfare; People and the
Family; Buildings, Houses, The Home, Clothes, Belongings and Personal Care;
Food, Drink and Farming; Feelings, Emotions, Attitudes and Sensations; Thought
and Communication, Language and Grammar; Substances, Materials, Objects and
Equipment; Arts and Crafts, Science and Technology, Industry and Education;
Numbers, Measurement, Money and Commerce; Entertainment, Sports and Games;
Space and Time; Movement, Location, Travel and Transport; General and Abstract
Terms.
15
'The symbols of natural languages are of two principal types, auditory and visual,
corresponding to spoken and written language respectively. It is convenient to treat the
concepts corresponding to spoken or written language or in fact any concepts as symbols too'
(Richens 1959:283). The symbols or concepts can be categorised and many 'being mediate
symbols representing further concepts'. Richens' categorisation is as follows (1959::283287):
Dixon has been reported to have claimed that each lexical item in a language can be classed
as one of a number of ‘semantic types’ (see discussion of this in Thompson 1989:246). He
introduces a broad classification of semantic types for nominal groups, and presents the
following smaller grouping for adjectives:
Dimension - big, little, long, wide, ...; Physical property - hard, heavy, smooth, ...;
Colour; Human propensity - jealous, happy, clever, generous, proud, ...; Age - new,
young, old, ...; Value - good, bad, pure, delicious, ...; Speed - fast, slow, quick, ..
Dixon (1982:16).
Together with,
Pre-adjectival modifiers: logical qualifiers (all, some, etc.), determiners (the, this),
possessives, superlatives, ordinal numbers, cardinal numbers.
Post-adjectival modifiers: origin/composition; purpose/beneficiary.
(Adjectives can also be classified as to whether or not they can form adverbs.) (Dixon
1982:24).
Dixon’s (1991) famous observations and recordings of Australian aboriginal languages has
led him to describe the following ‘areas of meaning’(superordinate categories?) for recording
observations about languages, particularly the lexical repertoire of the Yidiny, the almost
‘extinct’ aboriginal language of the Cairns-Yarrabah region of Northern Queensland
(1991:136-277):
16
Nouns: A. Body parts; B. Classification of people, and spirits; C. Moieties and kinship; D.
Mammals; E. Reptiles and amphibians; F. Birds; G. Fishes and other water creatures; H.
Insects and related creatures; I. Language, song, dance, noises; J. Artefacts; K. Food, fire,
water; L. Meteorological, celestial; M. Geography, place and house; N. Flora; O. Abstract
nouns;
P. Location qualifiers; Q. Time qualifiers;
R. Adjectives: R1-R4. Number and collectivity; R5-R10. Position and dimension; R11-R17.
Physical property and colour; R18-R22. Value, age, speed; R23-R31. Human propensity; R32R33. Other adjectives;
Verbs: S. Verbs of motion; T. Verbs of rest and giving; U. Verbs of affect; V. Verbs of attention;
W. Verbs of speaking, etc.; X. Corporeal verbs; Y. Other verbs, and adverbials
Z. Particles and Interjections
Each of Dixon’s ‘areas of meaning’ (items A to Z above) is subdivided into many sub-areas:
the body parts category is subdivided into 13 sub-areas; the language, song, dance and music
area (a semiotic category?) is subdivided into 4 sub-areas; the adjectives into 33 sub-areas
and the verbs of motion have been subdivided into 14 sub-areas. METAL, the machine
translation system, developed by the University of Texas at Austin, and used extensively by
the multi-national SIEMENS for translating documents into and from German, English and
Spanish, contains three semantic ‘types’: Nouns, Adjectives and Adverbs in its lexica. We
list some of the METAL semantic types, roughly the equivalent of Dixon’s ‘areas of
meaning’. (The underlined semantic subtypes either do not have an equivalent in Dixon or
these types cannot be found in Dixon’s categorisation)
Nouns: Body Part; Human; Social Institutions; Animals; Semiotic System; Plant; Abstract
Nouns; Concrete, Measure, Potent, Process. (METAL Manual p. 89)
Location and Temporal Nouns.
Adjectives: Count; Shape; Size/Volume, Measure; Quantity; Colour; Age; Indefinite (e.g. much,
little); Locative (e.g. internal, external); Order (e.g. first, last), Temporal (early, late).(METAL
Manual p. 142)
Adverbs: Connective (e.g. however); degree; locative (e.g. here), manner (e.g. abruptly);Temporal
(tomorrow, before); directional (e.g. upwards, downwards); inapplicable (e.g. perhaps, indeed).
(METAL Manual p. 189)
Spannu has discussed the semantic features of foods which is essentially ‘a list of semantic
attributes to be encoded within the relativised qualia structure for the noun subset (i.e.
foods)’. Such information may be ‘extracted from the definitions of foods within a number
of Machine Readable Dictionaries’. (1992:9). We list Spannu’s semantic attributes and
where possible, and relevant, refer to areas (and sub-areas) of meaning in Dixon: the bold
and italicised uppercase letter and numeral in parenthesis refers to Dixon:
17
AGEING: the age of an object (R19) (e.g. wine); AGENTIVE: the process by which an artefact is
created (e.g. "cook"(R14), "bake"); ALCOHOL: can be used to distinguish between alcoholic / nonalcoholic drinks or referring to quantity of alcohol contained in a drink; COLOUR: the colour of an
object; the intensity and brightness variations of colours (R17); CONSTITUENCY: encodes the
various constituents of an object; in particular, in the food subset; EVALUATIVE: individual or
social evaluation for distinguishing say between "a delicious food" (R20) and "popular, common
food" (K1-K2?); ORIGIN: the physical/biological source of food (K1?); ORIGIN_AREA (sic): the
geographical origin of an object; PHYSICAL: distinguishes between an abstract and a concrete
object; PHYSICAL_STATE: the state-of-matter of an object, e.g. "solid" (R13), "liquid", or “gas”;
QUANTITY: the quantity of a substance (e.g. much (R1)); SHAPE: the shape of an object with
reference to the dimensions of the space -- e.g. 2 or 3 dimensions (R6); SIMILAR: the similarity
between the object being represented and other objects; SIZE: the size of an object (R9); SMELL:
e.g. "aromatic" (R20), "flavoured"; TASTE: e.g. "bitter", "sweet" (R21); TEMPERATURE: e.g.
"cool", "warm" (R12); TEXTURE: e.g. "granular", "oily", "powdered" (R13); TRANSPARENCY:
the appearance of an object e.g. "milky", "turbid" (R18?), etc.; WEIGHT: the weight of an object
(R11).
3.2
Semantic Relations for General Purpose LanguageLyons (1977) emphasises the
‘paradigmatic’ and ‘syntagmatic’ relations: the former describe antonymy, synonymy,
partitive relations, hyponymy, and the latter focus on collocational relations. Lyons’
paradigmatic relations are what he calls ‘express contrast’, and at least the first six relations
(in the tables given below) emphasise associative relations between lexemes; the next two
express hierarchies, that is superordinate-subordinate relationship between lexemes and partwhole relations between lexemes; synonymous relations, what he calls ‘bilateral/symmetric
hyponymy’, express equivalence Lyons also mentions between lexemes. Collocations
between lexemes have been classified by Lyons as ‘syntagmatic relations’. The following
tabulation lists the semantic relations due to Lyons:
Semantic Relation
contrast
incompatibility
opposition
directional opposition
antonymy
converseness
complementarity
hyponymy
part-whole
synonymy
Elaboration
paradigmatic relations
general term to express opposition
relation holding between lexemes in
many-member sets
dichotomous/binary contrasts
implication of motion in one of two
opposed directions w.r.t. a given place
gradable opposition
R(x,y) = R'(y,x)
ungradable opposition
relation between a specific (subord.)
lexeme and a general (superord.) lexeme
hierarchical relation - whole-parts
bilateral/symmetric hyponymy
syntagmatic relation
collocation
18
Evans (1988) has argued that ‘we get information about paradigmatic and syntagmatic
relationships in different ways’. Paradigmatic information typically appears in standing or
generic sentences, that is sentences that are always true:
Adult male lions have manes.
A drake is a male duck.
Syntagmatic information, on the other hand, appears infrequently in occasional sentences,
sentences that describe particular situations:
The lion roared in fury at being caged.
We saw a family of ducks swimming in the pond.
(Evans 1988:7).
Palmer (1981) stresses the meaning relations hyponymy, synonymy and antonymy. And
antonymy, for Palmer, is a superclass, comprising gradation, complementarity, and relational
opposition. The latter express associative relationships between lexemes; hyponymy and
synonymy express hierarchical and equivalence relations respectively. The following
tabulation is based on a summary of the relations in Palmer’s book:
Relation
hyponymy
synonymy
antonymy
gradation
complementarity
relational opposition
polysemy
homonymy
homophony
homography
Elaboration
inclusion; class membership
sameness of meaning
oppositeness of meaning
degrees/comparative forms
incompatible terms
pairs exhibiting the reversal of a relationship between items
same word, different meanings
several words with same shape
spelt differently, pronounced same way
same spelling, different pronunciation
Lexicographers, who are mainly interested in ‘observing’ and ‘recording’ language (cf.
Sinclair), tend to use some of the seemingly more widely accepted terms when discussing
the semantic relations and semantic features relevant to the act of defining dictionary entries.
Jackson (1988) considers ‘meaning of words’ from two aspects: first, the ‘extra-linguistic
reality’, that is, ‘the meaning relation between the words of our language and the world of our
experience’ which involves ‘reference’ or ‘denotation’ and ‘connotation’ (1988:47-62).
And, second, ‘the meaning relations that hold within the vocabulary of a language between
the words themselves: lexical relations or, as they are often called, ‘sense’ relations. The
meaning of any lexeme may be described, then, both in terms of its reference or denotation
and in terms of its: both contribute to characterising a lexeme’s meaning’ (1988:64:77,
Jackson’s emphasis). Jackson discusses three principal lexical or ‘sense’ relations:
synonymy, antonymy and hyponymy:
19
Relation
synonymy
strict synonymy
stylistic’ synonymy’
technical’ synonymy
hyponymy
antonymy
gradable antonyms
complementary antonyms
converses/relational
opposites
Elaboration
same meaning
words which are ‘interchangeable in all contexts’
equivalents of ‘formal’ words in ‘coll. language: climb (AngloSaxon)-ascend (L)
equivalents of ‘technical’ terms in coll. language: cardiac-heart;
semantic-meaning
meaning of one lexeme included in the meaning of another
subject to comparison or qualification
denial of one member implies assertion of the other member
one member of the pair refers to the converse relation referred to by
the other member
Cruse (1986) discusses a wide range of relations, distinguishing between different types of
hierarchical relation, such as hyponymy, taxonomy, and meronymy, and considering various
kinds of synonymy and antonymy. He stresses that his discussion is in meaning rather than
about meaning. An important contribution Cruse makes is his discussion of what he terms
‘diagnostic frames’ for some of the relations; the frames are essentially what Lyons (1977)
refers to as ‘formulae’ for semantic relations, basically comprising phrases to denote a
particular relation; for example, the phrase X is a kind of Y denotes the relation of hyponymy.
Cruse discusses the variety of ways these frames may be expressed in natural language; for
instance, the relation of hyponymy may be expressed by such phrases as X is a type of Y, X is
a species of Y, and so on.
Relation
congruence relations
synonymy
cognitive synonymy
hyponymy
compatibility
strict compatibility
contingent compatibility
hierarchies
taxonomy
meronomy
antonymy
directional opposition
incompatibility
Elaboration
X & Y syntactically identical, & sentence with X has same truth
conditions to sentence with Y
inclusion of one class in another
overlap between classes
have at least one shared hyponym or hyponymous expression;
independently characterisable
not independently characterisable
classes with no members in common
Note that Cruse regards lexical semantic relations as ‘congruence’ relations. Furthermore,
Cruse distinguishes between ‘tree-structured’ hierarchies and the more tangled versions as
encountered in ‘non-branching’ taxonomies.
20
The Japanese Electronic Dictionary (JEDR) Project’s Semantic Dictionary has another, set of
relations. JEDR’s relations include taxonomic hierarchies (superordinate to subordinate
relations), whereas synonymy and causality are treated as conceptual relations, and the case
relationships include agent, object, manner, and implement.
Relation
conceptual,
case
inter-event relations,
semantic relations,
restriction relations
pseudo relations
Elaboration
Calzolari (1988) has discussed the creation of a ‘large [Italian] lexical data-base that
contrives dictionary information with that typical of a thesaurus’. The data base contains
‘syntactic information for more than 100,000 Italian words, as well as the synonyms,
antonyms, hyponyms, and hyperonyms used to relate words in a thesaurus’. Calzolari has
argued that the lexical database ‘can be structured and exploited in order to investigate the
semantic structure of the lexicon, to consider how database facilities can be used to capture
eventual generalisations from already available dictionary data’ (1988:75-77). Unlike a
‘traditional dictionary’ created by a lexicographer on the ‘purely extrinsic alphabetical
relation’, a database can be organised in a number of ‘different orderings’. These different
orderings can be realised, according to Calzolari, by organising the database according to
relations like, logical conceptual, semantic or lexical relations which are ‘discovered and
established between the entities’ (1988:79): ‘some of these relations can be extracted
automatically (or at least semi-automatically) from the Italian machine-dictionary
definitions’.
Relation
Hierarchical
Synonymy
Derivational
Other taxonomies
Terminological
Restriction or Case-type
Elaboration
hyponyms and superordinate relations expressible usually by IS-A
links
deals with suffixation: for each ending there are recurrent patterns, or
defining formulas or recurrent generic terms.
other than IS-A taxonomies: e.g. Part-Whole, Set-of, Process-of, Actof, Effect-of, Cause-of.
specialised language words; a nucleus of terms, keywords for
instance, form sublexicons the derivational and collocational
behaviour.
recurrent definitional patterns which are used to restrict the meaning
of modifications rel.the ‘genus’ term argument relation associated
with typical actions, e.g. locative, agent-case and object-case
relations.
21
3.3
Semantic Relations for Special Purpose Language
The foundations of the theory of terminology were developed by Eugen Wüster, mentioned
above. Inspired by the Prague School and the works of de Saussure, Wüster drew fruitfully on
his own experience as an engineer. Wüster's principles, based as they are on scientifictechnical LSP, require some measure of interpretation or modification, before they can be
applied to less taxonomic subject fields such as law, economics or education. Wüster's
principles are of course based on certain assumptions about human knowledge and its use:
bodies of knowledge based on abstract Platonic concepts (prelinguistic 'elements of thought')
described in a highly nominalised fashion. Alternative models may be descriptive rather than
prescriptive and denotative rather than connotative, and have a very important place in the
organisation and use of terms and consequently that of knowledge. However, the followers of
Wüster's tradition have developed a large number of operational term banks for a variety of
specific subject domains, and for this historical reason alone there is some justification in the
claim that it is still the concept and its elaboration within the framework of a system of
concepts which plays the central role in the theory of terminology, within a particular subject
field. The equivalent of ‘semantic relations’ proposed by Wüster can be summarised as
follows:
Relation
Elaboration
logische Beziehungen (zwischen zwei Begriffen)
logische Unterordnung Oberbegriff - Unterbegriff
logische Nebenordnung
logische Diagonalbeziehung
logische Beziehungen (zwischen zwei und mehr Begriffen)
logische Leiterbeziehung
logische Reihenbeziehung
logische Verknüpfung
ontologische Beziehungen (zwischen zwei Begriffen)
Bestandsunterordnung
Bestandsnebenordnung
ontologische Beziehungen (zwischen zwei und mehr Begriffen)
Bestandsleiterbeziehung
Bestandsreihenbeziehung
Bestandsverknüpfung
The Wüster-inspired terminologists begin by using a ‘standardised’ definition of ‘concept’
‘concept’: Any unit of thought, generally expressed by a term, a letter
symbol or by any other symbol (ISO/R 1087: page number).
Heribert Picht and Jennifer Draskau are among the leading proponents of the Platonic, and
now Wüsterian, school of terminologists. For these authors 'the system of concepts in
terminology is not a goal in itself, nor an intellectual pastime. On the contrary, it is an
indispensable aid in the elaboration of a terminology' and they enumerate the benefits of
using a 'system of concepts':
22
1. The reconnaissance of the structure of the inventory of concepts of a special subject
field as a preliminary to a systematic elaboration.
2. The recognition of the exhaustiveness of an inventory of concepts.
3. The comprehension of the relationship between concepts which may be important for
the formation of terms.
4. The recognition of the degree of congruence between the systems of concepts of
different languages'; this in turn is indispensable for the recognition of equivalence.
5. The representation in a systematically organised dictionary, of the results of
terminology work; the dictionary form may be available in hard copy of on-line, in a term
bank. (1986:92)
Picht and Draskau then go on to describe ‘term-concept relations’, ‘logical’ and ‘ontological’
relations between concepts which may be used in the development of a term bank. Their
description has parallels in the description of ‘semantic relations’ (between words and
sentences) outlined in the previous section.
Relation
term-concept relations
logical relations
ontological relations
Elaboration
monosemy
polysemy
synonymy: quasi-synonymy
synonymy:pseudosynonymy
equivalence
homonymy
implication
intersection
disjunction
negation
opposition
part-whole
causal association
temporal association
genetic association
production
transmission
Picht and Draskau’s description of relations between terms and concepts is considerably
more structured than the earlier description due to Felber (1984).
3.4
X-bar semantics
Chomsky has argued that it is impossible to state a transformational derivation with any
generality and that the sharing of properties should instead be expressed by decomposing
syntactic categories into a feature system - just as properties shared among phonological
segments of a language are expressed in terms of distinctive features.
With due modesty, Jackendoff states that 'while this program of research still cannot be
considered complete, the point of similarity among syntactic categories that form the
23
underpinnings of X-bar theory are undeniable' (1990:22). The 'conceptual structure' [system],
according to Jackendoff, comprises three major subsystems, that is, categorial and functional
subsystems, and [subsystems] to deal with phenomena' like 'aggregation' (plural meaning, for
instance) and 'boundedness' (for example, boundedness of 'temporally unbounded processes,
and that repeated events behave like processes). (check)
Categorial subsystems:
The essential units of conceptual structure are conceptual
constituents, each of which belongs to one of a small set of major ontological categories (or
conceptual "parts of each") such as Thing, Event, Action, Place, Path, Property, and
Amount. Although 'all quite different in the kind of reference they pick out, formally
(algebraically) they have a great deal in common'. Jackendoff then elaborates on his
'primitives' as follows:
'1.
Each major syntactic constituent of a sentence [...] maps into a conceptual constituent in
the meaning of the sentence [e.g. NP can express a Thing, an Event, or a Property; PP
can express a Place, a Path].
Each conceptual category supports the encoding of unit not only on the basis of
linguistic input but also on the basis of the visual (or sensory) environment.
Many of the categories support a type-token distinction.
Many of the categories support quantification over 'Things', 'Actions' and 'Places'
through the closed-set lexical items, like every, everything, and anyplace respectively.
Each conceptual category has some realisations in which it is decomposed into a
function-argument; each argument is in turn a conceptual constituent of some major
category: (e.g. attributional: John is Tall; relational: John loves Mary; temporal: John
2.
3.
4.
5.
leaves Thing Property Thing, Thing Event/Statefunction Statefunction Event tried to function.)
6.
The conceptual structure of a lexical item is an entity with zero or more open argument
places. The meanings of the syntactic complements of the lexical item fill in the values of
the item's argument places in the meaning of the sentence. (Jackendoff 1990:22-24).’
According to Jackendoff, the 'general picture' which emerges from the above similarities
between the various conceptual categories, is that of 'X-bar Semantics'. 'None of the major
conceptual categories' can be insightfully reduced to the others but they share 'important
formal properties' (1990:24).
Thus, parallel to the basic formation rules of X-bar syntax, a basic function rule for conceptual
categories can be stated as:
X-bar syntax
X-bar semantics
→ #
#
→ ($%)*+),&$$%&%'" →
.$)'
/!$%&%'#! $%&%' ! 0$%&%' 1112
→ ±! ±"
3
24
4
More specifically, within the constraints of the above schema, each conceptual category
permits a variety of elaborations:
"
/ "2
*+
"
{
}/
{ }
+%,
"
"
{
{
($% / "! "2"
}
($% / "! "2"
%+% / " "2"
}
%+% / "! "2"
%+% / "! "2"
25
2
" →
($%
/
{ }
"
!
2
(EXT is an abbreviation for the spatial extension of linear objects along a path pp 44.)
The 'conceptual functions', that is, GO, STAY, BE, ORIENT, EXT, CAUSE, TOWARD,
TO, FROM, CAUSE, can have one, two or three arguments. Jackendoff is 'suspicious' that
some of these functions, like STAY which can be regarded as 'perhaps some sort of durational
form of BE' may not be 'conceptual primitives'. That some of the conceptual primitives, i.e.
functions, can be decomposed into constituent features can be understood in terms of an
evolutionary example from chemistry: the elements of the periodic table have been identified
but [one] has not yet probed the structure of the atom - 'there is significant decomposition
down to a certain level, but one should not regard it necessarily as complete' (1990:44).
Spatial
be } from
keep} to
go
change
across
during
Temporal
none
marked
marked
-
Possession
none
Assumption
none
marked
Scheduling
none
marked
-
marked
marked
-
Location
none
marked
-
In order to account for verbs in English, like point, surround, stood, hide, shelter, block,
support, face and sit, which alternate between a STATE reading and an EVENT reading, the
familiar INCHoative relation relates the two readings - the EVENT reading described a
change taking place whose final state is the STATE reading (1990:74-75):
" →
($%
/2"
Furthermore, in order to deal with Actor-Patient relations in the 'action tier',
Sue
hit
Theme Goal
Actor
Fred
(thematic tier)
Patient
(action tier)
Jackendoff (1990:126-127) introduces AFF ("affect") as an additional mainstream function
alongside the thematic functions. The first argument is the Actor and the second is the
Patient:
26
The car hit the tree
/ "! ""2"
,5%& &5
%&$ &5
→
/ "! "2
($%
Aggregation and Boundedness
$%&%'
6&$-7*+5 ,&$6&$-7*+5 ($%
8&7
796%+$
5666
*75+* ,&$-6
*75+* ($%6
3.5
ISO Standards
We referred above to ISO 1087. This Standard contains the most recent ISO approved list of,
what might loosely be referred to in general language as semantic relations. The Standard
focuses on relations between concepts and relations between terms and concepts:
Relation
Elaboration
relation between concepts
hierarchical
Relation between concepts established by division of a superordinate concept into
relation
subordinate concepts forming one or more levels, or by the reverse process.
generic relation Hierarchical rel. which is based on the partial identity of the intentions of generic,
specific and co-ordinate concepts.
partitive
Hierarchical rel. in which the superordinate concept refers to an object as a whole and
relation
the subordinate concepts to parts of it.
sequential
Rel. of dependence between concepts referring to objects which have a spatial or
relation
temporal contiguity.
pragmatic
Rel. bet. concepts which can be established on the basis of thematic connections.
relation
Relation
Elaboration
relation between terms and concepts
monosemy
Rel. bet. designation and concept in which the former designates only one concept
mononymy
Rel. bet. designation and concept in which the concept has only one designation.
synonymy
Rel. bet. designations representing only one concept in one language
polysemy
Rel. bet. several concepts sharing certain characteristics and their common
designations.
homonymy
Rel. bet. designations and concepts in which identical designations represent
diff..........concepts.
equivalence
Relation between designations representing the same concept in different languages.
The notion of "concept" provokes an ideological reaction among various schools of
terminologists. Scholars involved in the study of (multilingual) translation processes and
theories, like Kurt Kohn, emphasise the so-called ‘sense relationships’ which prevail in the
27
terminology of special or sublanguages. We refer to Lacey for an elaboration of the so-called
"sense relationship". “Sense” refers to the system of linguistic relationships (sense relations)
which a lexical item contracts with other lexical items - the paradigmatic relationships of
synonymy, antonymy etc. and the syntagmatic relationships of collocation.
Juan Sager in his ‘A Practical Course in Terminology Processing’ (1990) proposes a hybrid
scheme which contains elements of the Wüsterian tradition combined with causality, objectoriented, process-oriented and material relations between terms and between terms and
concepts. Curiously, Sager divides his scheme into the well-known relations and a very
generic ‘miscellaneous relations:
Relation
generic relationship
partitive relationship
cause-effect
material-relations
process-relations
object relations
Elaboration
conceptual relations
generic - specific hierarchical order
one part and constituent parts
miscellaneous relations
material-product
material-property
material-state
process-product
process-instrument
process-method
process-patient
phenomenon-measurement
object-counteragent
object-container
object-material
object-quality
object-operation
object-characteristic
object-form
activity-place
However, since this word (term, sign, symbol and so on) has been discussed extensively, it is
perhaps important to expand on the ISO R/1087 definition above and also to bring in the
definition collated by Lacey (1986) from the philosophical point of view. Our intention is
impartiality.
Concepts may be the mental representation not only of beings or things (as
expressed by nouns), but, in a wider sense, also of qualities (as expressed
by adjectives or nouns), of actions (as expressed by verbs or nouns), and
even of locations, situations or relations (as expressed by adverbs,
prepositions, conjunctions or nouns).
A concept may represent only one individual object or - by "abstraction" comprise all individuals having certain characteristics in common.
Furthermore a concept may arise from the combination of other concepts,
even without regard to reality. The number of concepts (represented by
terms) which may be combined to form a new concept (term) is limited by
28
the fact that in a proposition a concept can only be either subject or
predicate, but not comprise both.
Lacey notes that 'concept' has taken over some uses of the ambiguous term 'idea', perhaps
partly because 'idea' suggests images etc. To have a concept of anything is to be able to
distinguish it from other things, or be able in some way to think or reason about it. Concepts
are connected with universals. On one view concepts are 'of' universals, so that to have a
concept of, say, dog, is to be related to a non-material object like a Platonic form. 'Concept
of dog' is perhaps best taken as a single linguistic unit, like 'dog-concept', so that one is not
tempted to seek some entity that 'dog' stands for.
The above semantic relations and semantic features draw from the three major areas of study
of meaning, that is, linguistic semantics, philosophical semantics and logical semantics. We
now draw the attention of the reader to some of the seminal work in the study of meaning,
particularly with the intention of emphasising the choice of the so-called ‘semantic
primitives’ used in the description of semantic features and semantic relations. (The socalled universal semantic relations, semantic roles, will not be discussed below. See
Appendix A for more details).
29
4.
Psychology and ‘Semantic Primitives’
The discussion of 'the organisation of knowledge' in Cognitive Psychology textbooks is
generally based on the so-called 'object concepts', 'relational concepts' and 'knowledge
structures, including events and states'. These terms are related to a variety of terms used in
philosophical, logical and linguistic semantics. There is, for instance, extensive use of
'hierarchical relations', that of taxonomic, generic and part-whole relations, in the cognitive
science literature (Eysenck and Keane 1990). 'Real world', 'abstract' and 'concrete' objects
are assigned various [semantic] features. The more direct use of 'semantic relations' has been
explored in the literature on categorisation.
: *+5- -57&$-6 ; $%6 +5
6%57%758 +$8 768 &$ (+5&76 *<
-$&%&( %+6.6! *&. 59*6*(&$-!
*+5$&$-! *+$-7+- 587%&$ +$8
5,$6&$
: 8&;;5$% $%&%&6 % 9 -578
%-%,5 +$8 ,: %,6 +%-5&6 +5 5*+%8
% $ +$%,5 ,&5+5,&+**'
5 <+*! A(5'8+'A ,'6&+*
+$8 +96%5+% $%&%&6 +&$*' $7$6
*&. 9&58! ,+&5! ;75$&%75
+5&76 8'+8&! $+8& +$8 %5&+8&
5*+%&$6 9%:$ +$8 :&%,&$ %,
9=%6! ($%6! 6%+%6> +766 /8'+8&2? /$+8&2? &6
9%:$ +$8 @ /%5&+8&2
$*78&$- A(5'8+'A ($%6 +$8
5-+$&B+%&$ ; 6C7$6 ; %,6
($%6 &$% *+$6 5 %,5 58&%&(
.$:*8- 6%57%756
,+%+
5+6
5&%6
! " #$$%&" '( ) !* +"
,-$"
4.1
Categorisation and Classification
Categorisation is regarded as a ‘fundamental cognitive process because every [human]
experience is in some sense unique. [..] However, if each experience were given a unique
mental representation, we would be quickly overwhelmed by the sheer complexity, and we
could not apply what we had already learned to deal with new situations. By encoding
experiences into an organised system of categories, we are able to recognise significant
commonalities in different experiences. A category system allows us to derive further
30
information about an object that has been assigned to a category’ (Glass and Holyoak
1986:149). These authors argue that category definitions can be based on ‘enumeration,
appearance, functions and relations’ and that there are ‘natural categories’ which refer ‘to the
categories used by people in everyday life’ (1986:160). These ‘everyday life categories’
include ‘colour categories’ and ‘basic level categories’. The latter categorisation is at ‘the
level at which people naturally divide the world into alternative categories’ and ‘people’ not
only ‘maximise the perceptual similarities among instances of the same category’, but also
‘maximise the differences between instances of different categories’ (1986:166).
Categorisation and classification are central to work in learning, memory and intelligence.
Eleanor Rosch’s work on ‘natural categories’ provides ‘considerable support for the idea that
a person’s information about his or her normal environment tends to be organised in a
hierarchical structure with relatively higher-order categories (e.g., living thing, animal,
inanimate object) near the top of the hierarchy, basic object categories (tree, table, pencil) at
an intermediate level, and finer subordinate categories at the lowest levels.’ (Estes 1982:215216). Dehn and Schanck’s (1982) comparison and contrast of ‘artificial and human
intelligence’ includes a discussion of the inter-dependence of ‘memory organisation’ and
categorisation in the context of ‘natural language processing’: ‘The ability to understand an
input crucially depends on one’s original categorisation of ideas and events to which those
inputs refer. If one’s categorisation is “unintelligent”, that is, if one has made insufficient
multiple categorisations of prior events in memory as a guard against future needs , then one
will find it hard to understand many statements that correspond only partially or abstractly to
one’s prior experience. The transmuting of an initial categorisation into a categorisation in a
different domain is thus an important
of intelligence’ (Dehn and Schanck 1982:379).
Medin and Smith (1981) have discussed ‘Categories and Concepts’ by describing in depth ‘a
probabilistic view of concepts’ in order to account for a number of psychological
experiments on categorisation. The experimental data cannot be ‘explained’ by the use of the
so-called ‘Aristotelian’ classical model of ‘concepts’. They begin by noting that ‘without
concepts, mental life would be chaotic’ and describe ‘kinds of properties for concepts’
including “component versus holistic properties and ‘dimensions and features’ of concepts
(1981:10-15). ‘A component property is, roughly, one that helps to describe an object but
does not usually constitute a complete description of an object. [....] In contrast, a holistic
property offers a complete description of the object’ (1981:11). ‘If we decide to represent
object concepts in terms of components, we have a choice of how to characterise these
components -- either by quantitative components, called dimensions, or by qualitative
components, called features’. Furthermore, ‘a set of features should not only make relations
31
apparent but ideally should exhaust all potential relations between the concepts of interest’
(1981:15).
4.2
Psychological 'reality' of lexical semantic relations
Markowitz has used ‘lexical semantic relations, such as function, part-whole, and agent’ to
obtain the views of 76 experimental adult-subjects about ‘prototypicality and membership
gradation’ (1988: 239-260). The term ‘graded membership’ is used to express the so-called
‘probabilistic view of concepts’. Markowitz’s experiments were designed to expand ‘the
understanding of graded set membership through specific changes in methodology and
analysis’. The changes refer to the use of ‘qualitative data’ rather than quantitative data (e.g.
response times) and to the use of a more diverse sample of adults. Other studies, Markowitz
claims, focus on homogeneous populations, like college sophomores; and Markowitz has
used a diversity of (superordinate level) categories, including ’highly non-visual categories,
part-whole categories, and activity categories’ to provide ‘some checks and balances on the
application of findings, particularly those regarding the importance and function of specific
semantic relations’ (1990:240-241) (Our emphasis).
Markowitz had 21 test categories divided into five groups: ‘Naturally occurring objects’
(seven categories); ‘Manufactured objects’ (nine categories); ‘Highly non-visual categories’
(three categories); and ‘one part-whole’ and ‘one activity category’ (1990:242):
Naturally
Occurring
Animal (D)
Bird (Basic Level
F)
Flower (N)
Fruit (N?
Insect (H)
Tree (N?)
Vegetable (K)
Manufactured
Highly Non-Visual
Part-Whole
Activity
Clothing
Footwear (J)
Drink (??)
Fuel (K)
Body Part (A)
Sport (L)
Furniture (J)
Kitchen Utensil (J)
Musical Instrument
(J or L)
Tool (J)
Toy (J)
Vehicle (J)?
Weapon (J)(
Seasoning (K)
(The italicised entries refer to Dixon’s categories, cf. section 1.2)
The subjects were shown pictures, ‘member cards’, of objects and events and were asked to
categorise these objects and events. The categorisation was ‘observed’ by first eliciting a
‘folk definition’ of the picture -- ‘explain the category as if they were talking to a child or to
someone from another planet; second, if necessary, the interviewer was asked to probe the
‘definition; third, the subject was asked to rank a set of category cards on the basis of
‘typicality’ and encouraged to ‘verbalise’ while ranking; and finally, the subjects were asked,
32
after the ranking, which, if any, of the items were not members or were questionable
members of the category. The subject interviews were content analysed ‘to identify the
semantic relations which played the greatest role in typicality and restriction of category
membership’ (1990:243). These semantic relations are shown below in Table XXXXX
(1990: 244-245):
Table XXXX
Relation
AGENT
Elaboration
Example
A lexical relation which signals the
Birds fly; People drive vehicles.
instigator of an action
A relation which expresses oppositeness or Feathered vs. furry vs. scaly; Big
ANTONYMY
complementarity often sets two or more
vs. small.
attributes against each other.
A relation expressing goal, use, or purpose. Clothing is for warmth.
FUNCTION
In English it is sometimes represented by
"for".
You use fuel to heat your house;
A lexical relation showing an inanimate
INSTRUMENT
You eat with a kitchen utensil.
object which is involved in an action. In
English it is often expressed by "with" or the
verb "to use".
A relation which signals place or position. Animals walk on land; Body parts
LOCATIVE
are inside the body.
In English it is frequently signalled by
prepositions.
Birds are small; Furniture is made
MODIFICATION A general attributive relation expressing
of wood.
such things as colour, size, texture,
evaluative judgement, source, and material.
In English it typically, but not universally,
appears as an adjective.
A lexical relation which identifies something You eat chicken; People drive
OBJECT
as the recipient of an action.
vehicles.
A relation which identifies something as
A vehicle has a motor; A petal is
PART-WHOLE
being a segment or a portion of something part of a flower.
else. In English it is often expressed by
"part of", the verb "to have", and the
possessive.
A bug is an insect (meaning the
A relation which signals equivalence
SYNONYMY
word "bug" word is just another
between words or verbal expressions. In
English it often appears as the verb "to be". for "insect"
A relation showing the membership of an
Ants and fleas are kinds of insects.
TAXONOMY
individual in a set or of a group of
A bug is an insect (meaning a bug
individuals in a larger group. In English it is is a kind of insect)) Care must be
often expressed by the verb "to be" or "is a taken to distinguish it from
synonymy. (Our emphasis)
kind of".
Markowitz’s results can be summarised as follows. The ‘most important’ semantic relation
was that of Modification: a major factor in the ranking and restricting membership for 17 of
the 21 categories; the (Modification) size relation and beauty/ugliness contrast (‘ugly insects
were judged more insect like than beautiful insects, whereas beautiful flowers and birds were
seen as more representative of their category than plain category members). The Part-whole
relation was important in all categories of ‘naturally occurring’ objects except the food-forms
fruit and vegetable, the body part category, the drink and seasoning categories, and the
vehicle category. The other important categories found by Markowitz were function, agent
33
and object. The five semantic relations, modification, part whole, function, agent and object,
operated jointly with other relations to rank and/or eliminate member-candidates. Although
other semantic relations were ‘present’ in the data, i.e. inferred through the content analysis
of subject interviews, they were rarely used to rank or limit membership (1988:250-256).
Markowitz concludes by noting that ‘the use of semantic relations to analyse qualitative data
provided clear support for the hypothesis that distinct category types exist and that they differ
with regard to the characteristics which contribute to typicality’. Furthermore, Markowitz’s
study ‘shows that in order to understand membership gradation fully, research in this field
must use powerful, highly-organised analytic tools, like semantic relations. The fact that
semantic relations can also be used to construct complex models of concept structure is an
added benefit.’ (1988:258).
4.3
Chaffin and Hermann
Table 2. Examples of thirty-one semantic relations.
(from five families listed in the order shown in Figure 1 from Chaffin and Hermann,
1984)
I. CONTRASTS
II. SIMILARS
Contrary
Contradictory
old-young, happy-sad
alive-dead, male-female
Reverse
attach-defend, buy-sell
Directional
Incompatible
front-back, left-right
happy-morbid, frankhypocritical
hot-cool, dry-moist
Asymmetric
contrary
Pseudoantonym
Attribute similar
Synonymity
Dimensional
similar
Necessary
attribute
Invited attribute
Action subordinate
car-auto, buy-purchase
smile-laugh, annoy-torment
bachelor-unmarried, towerhigh
food-tasty, cut-knife
talk-lecture, cook-fry
popular-shy, believe-deny
rake-fork, painting-movie
III. CLASS INCLUSION
IV.CASE RELATIONS
Perceptual subord. animal-horse, flower-rose
Functional subord. furniture-chair, tool-hammer
disease-polio, emotion-fear
State subord.
game-chess, crime-theft
Activity subord.
state-New Jersey, countryGeographic
Russia
subord.
Germany-Hamburg, AsiaPlace
China
Agent-action
Agent-instrument
Agent-object
Action-recipient
Action-instrument
artist-paint, dog-bark
farmer-tractor, soldier-gun
baker-bread, sculptor-clay
sit-chair, hunt-prey
cut=knife, drink-cup
V. PART-WHOLE
Functional object
Collection
engine-car, tree-leaf
forest-tree, fleet-ship
Group
choir-singer, facultyprofessor
mile-yard, hour-minute
Measure
Ingredient
Functional
location
Organisation
34
table-wood, pizza-cheese
kitchen-stove, house-dining
room
college-admissions, armycorps
5.
Artificial Intelligence: Knowledge and Semantics
5.1
Networks and 'Meaning' Representation
Ross Quillian (1966; 1968) was among the early AI workers to develop a computational
model which represented 'concepts' as hierarchical networks. This model was amended with
some additional psychological assumptions to characterise the structure of [human] semantic
memory. Collins and Quillian (1969) proposed that:
• Concepts can be represented as hierarchies of inter-connected concept nodes (e.g. animal,
bird, canary)
• Any concept has a number of associated attributes at a given level ( e.g. animal --> has
skin; eats etc.)
• Some concept nodes are superordinates of other nodes (e.g. animal >bird) and some are
subordinates (canary< bird)
• For reasons of cognitive economy, subordinates inherit all the attributes of their
superordinate concepts
• Some instances of a concept are excepted from the attributes that help [humans] to define
the superordinates (e.g. ostrich is excepted from flying)
• Various [psychological] processes search these hierarchies for information about the
concepts represented
!
&6 +
&6 +
&6 +
&6 +
&6 +
! " "
' . * /* #$0$&"
35
Minsky has discussed various questions related to 'learning meaning' in his well-received
book, ‘The Society of Mind’. The author emphasises the notion of 'structure and function'
and argues that 'I'm sure it's not an accident that we so often frame our thoughts in terms of
familiar spatial forms' (1985:122). Minsky concludes the discussion of 'learning meaning' by
arguing that 'what people call "meanings" do not usually correspond to particular and definite
structures, but to connections among and across fragments of the great interlocking networks
of connections and constraints among our agencies. Because these networks are constantly
growing and changing, meanings are rarely sharp, and we cannot always expect to be able to
"define" them in compact sequence of words." (1985:131).
5.2
Relational Graphs and Semantic 'Primitives'
Sowa begins his seminal book on 'Conceptual Structures: Information Processing in Mind
and Machine', by remarking that 'Any representation of knowledge and meaning inside a
computer must embody some philosophical assumption' and that writing 'a program without
analysing the issues (philosophical assumption) is to make a blind choice instead of a
reasoned commitment' (1982: 1). Sowa then goes to develop a knowledge representation
language - so-called conceptual graphs - and remarks that "For AI, programmers needed a
theory of performance that could support communication between people and machines. In
AI systems, conceptual graphs are widely used to represent meaning" (1982: 8). For
instance, for the sentence "the man consults lexica", Sowa's conceptual graph will be where:
"the boxes are called concepts and the circles are called conceptual relations. Concepts
represent any entity, action or state and conceptual relations show the roles that each entity
plays".
Historical survey of conceptual graphs: Lecture Notes picture
A number of authors in AI have addressed the question of the 'concept'-based organisation of
knowledge and we use two examples to illustrate this. Firstly, we discuss a highly
nominalised system proposed by Sowa (1984): 'conceptual graphs'. Then we consider a verboriented organisation of knowledge proposed by Schank (1975): Conceptual Dependency
Grammar. Both deal with conceptual primitives at a very general level. Sowa is more
interested in the highly nominalised conceptual graphs for knowledge-based systems,
whereas Schank developed a set of relations based on verbs of action and motion.
36
Action: 'A dog is greedily eating a bone'
+-$%D
9=D
+%
$
+$$5D
58
Relational Graph due to Sowa (1984)
-
D
⇔ D
$
&$D
58'
Relational Graph due to Schanck (1977)
37
Action +Event: 'While a dog was eating a bone, a cat passed by'
/2
-
+-$%
+%
9=
$
+-$%
/2
/$%&2
/:,&*2
9=
+%
+-$%
+66 9'
/2
A verb-centred Relational Graph due to Schanck1977)
Action+Event+State(Belief): ' Sue thinks that Bob believes
the dog is eating a bone
56$> 7
<5&$
,&$.6
9=%
56&%&$>
56$> 9
<5&$
*&(6
9=%
56&%&$>
-
+-$%
9=%
+%
$
A network with nested propositions (Sowa 1990)
Sowa derives inspiration from 'linguistics, psychology and philosophy' (1982: 69) to specify
conceptual graphs formally which is a 'notation for representing knowledge. But to serve as a
basis for thinking, they [conceptual graphs] must be used in computation' and Sowa specifies
'rules of inference for exact deduction, schemata for plausible reasoning, and actors for
38
general computation' (1982: 127-206): the logical properties for the 'computable' conceptual
graphs. Sowa's linguistically plausible approach with its descriptive links to philosophy and
psychology and its logically consistent outline shows that conceptual graphs, based on the
abstract notion of concepts, is of relevance not only to AI but also to terminography.
5.2.1
Conceptual Graphs
Conceptual graphs form a knowledge representation language based on the one hand in
linguistics, psychology and philosophy, and data structures and data processing techniques on
the other. A conceptual graph consists of concept nodes and relation nodes. The concept
nodes represent entities, attributes, states, and events . The relation nodes show how the
concepts are interconnected. Sowa's claim is that his conceptual graphs, comprising the
nodes, are essentially a mapping of perception onto an abstract representation and reasoning
system.
Sowa argues that "a conceptual graph has no meaning in isolation.
Only through the
semantic network are its concepts and relations linked to context, language, emotion, and
perception." To illustrate the above point, consider the conceptual graph for a cat sitting on a
mat :
Words
Rules for assembling percepts
Grammar
Rules
Percepts
CAT
STAT
SIT
LOC
MAT
Episodes
Procedures
Emotions
Type Defintions
39
Concrete concepts are associated with percepts for experiencing the world and motor
mechanisms for acting upon it. Some concepts are associated with the words and grammar
rules for a language. A hierarchy of concept types defines the relationships between concepts
at different levels of generality. Formation rules determine how each type of concept may be
linked to conceptual relations. Each conceptual graph is linked to some context or episode to
which it is relevant. Each episode may also have emotional associations which (directly or
indirectly) confer emotional overtones.
Sowa has formulated Conceptual Graphs in terms of:
Conceptual Graphs
Concept Types
Conceptual Relations
Assertions
Negation
Abstraction and Definition
Canonical-(or sensible) graphs
Generalisation and Specialisation
Copy, Restrict, Join and Simplify
Schemata and Prototypes
Concept Types: 'Semantic Properties' Sowa has described six "primitive concept types"
which are to be used in conceptual graphs:
Attribute:
an attribute is a quality of an entity,
Entity: includes all physical objects as well as abstractions
Event: acts by animate agents as well as happenings where agents are not present
Information:
anything that can be communicated
Measure:
quantities related to entities, very much like names associated with concepts
State:
duration, related to concepts, which are in a flux
Sowa has defined 62 terms used in his book, in terms of these primitives. The concepts and
certainly the primitives cover a spectrum from the concrete, such as "measure" and "state" to
the more abstract, such as "information".
Some examples of hierarchies in Sowa
ENTITY
PHYSOBJ
<Ø
< ENTITY
TELEPHONE
< PHYSOBJ
INFORMATION
<Ø
MESSAGE
< INFORMATION
PROPOSITION
< INFORMATION
ENTITY has no supertype
A Physical Object is a type of
entity
A Telephone is a type of physical
object
INFORMATION has no
supertype
A message is information in the
role of being communicated
A type of symbolic information
as opposed to images
40
The hierarchy is partially ordered in that the hierarchy is a binary relation which is reflexive,
antisymmetric and transitive. The hierarchy must form the mathematical structure known as
a lattice: every two types must have at most one maximal common sub-type and one minimal
common super-type.
Conceptual Relations Sowa's definition of conceptual relations is based on the assumption
that 'the process of perception generates a structure u called a conceptual graph in response
to some external entity or scene e'. The graph comprises 'a linkage of concepts c1, c2 ...cn'
and describes the way percepts are assembled. Conceptual relations 'specify the role each
percept plays: one percept may match a part of an icon to the right or left of another percept;
a percept for a colour may be combined with a percept of shape to form a graph that
represents a coloured shape.' (1984:70-71).
Conceptual relations may have any number of arcs: if a relation has n-arcs then it is said to
be n-adic: Dyadic Relations are among the most common:
Relation
Dyadic Relations
accompaniment
attribute
characteristic
content
part
support
possession
manner
result
source
agent
recipient
destination
path
material
argument
causation
Monadic Relations
negation
past
possible
Triadic Relations
between
Elaboration
links Entity:*x to Entity:*y
links Animate to Entity:
links Act to Attribute
links Act to Entity
links Act to Animate
links Act to Place
links Act to Substance
links Function to Data
links State:*x to State:*y
George left with Barbara
The rose is red
Ronnie is 100 years old
A baby is in a pen
A finger is a part of a hand
The frost is on the pumpkin
Nikita's watch stopped
The taxi arrived quickly
Eric built a house
The pail was carried from the
shed
Eve bit an apple
Toys were given to Ruby
Bob went to Danube
The car was driven via Albany
Books are made of paper
SQRT(16) = 4
If you are wet, it is raining.
links to a PROPOSITION
Kirby did not eat an apple (NOT
(Kirby apple)
Judy left. (PAST (Judy here)
The baby can talk.
links Entity:*x to Entity:*y
&Entity:*z
The space between two bricks.
Conceptual Graphs are finite, connected, bipartite graphs. The graphs are finite because any
graph (in 'human brain' or 'computer storage') can only have a finite number of concepts and
41
conceptual relations. The graphs are connected because two parts that are not connected
would simply be called two conceptual graphs. The graphs are bipartite because there are
two different kinds of nodes: concepts and conceptual relations, and every arc links a node of
one kind to a node of another kind.
Canonical Graphs: A conceptual graph is a combination of concept nodes and relation
nodes where every arc of every conceptual relation is linked to a concept. This could lead
sometimes to sensible statements like 'a bunny is sitting on a mat' and at times will lead to
nonsense like 'colourless green ideas sleep furiously':
sleep
AGNT
idea
Colourless green ideas sleep furio
Concepts
, Some act of sleeping,
which is an idea,
which has a color green
COLR
green
usly
Concepts
Sowa distinguishes nonsensical graphs from those 'that represent real or possible situations in
the external world' by declaring the later as canonical. Certain conceptual graphs are
canonical. New graphs may become canonical or be canonised by perception, formation
rules, or through 'insight'.
New canonical graphs may be derived from other canonical graphs by so-called formation
rules:
copy,
restrict,
join
and
simplify
Formation rules are the (generative) grammar of conceptual structures. All deductions and
computations on canonical graphs involve some combination of them. These rules are rules
42
of specialisation: they involve specialisation by selectional constraints. They are not rules of
inference-- rather templates which are manipulated in order to incorporate new knowledge.
5.2.2
Conceptual Dependency Grammar
Conceptual dependency (or CD) is a theory of how to represent the meaning of natural
language sentences in a way that: (a) such a representation facilitates the drawing of
inferences from the sentences, and (b) is independent of the language in which the sentences
were originally stated.
comprehension project. Schank's claim was that sentences can be translated into basic
concepts expressed as a small set of semantic primitives (see Figure below). Conceptual
dependency allows these primitives, which signify meanings, to be combined to represent
more complex meanings. Schank calls the meaning propositions underlying language
"conceptualisations". An example of an active conceptualisation is:
Action Object Direction (Instrument);
an example of a stative conceptualisation is:
Object (is in) State (with Value)
(Schank and Abelson, 1977: 12).
The use of such semantic roles in the pre-syntactic representation of propositional content of
sentences has clear parallels with Fillmore's (1968) concept of deep case.
The proponents of the CD theory argue that 'the CD representation of a sentence is built not
out of primitives corresponding to the words used in the sentence, but rather out of
conceptual primitives that can be combined to form the meanings of words in any particular
language'. Consider the conceptual dependency embedded in a simple sentence and its
equivalent CD representation:
to
R
John
ATRANS
book
from
John gave the man a book
Building Blocks of the Conceptual Dependency Grammar:
43
man
John
Primitive Acts
Primitive
conceptual
categories
Diagrammatic
Conventions
'Conceptual
Tenses'
ATRANs indicates transfer (of possession)
o indicates object case relation
R indicates recipient case relation
Arrows indicate the direction of dependency; Double arrow
indicates two way link between actor and action
p denotes past
o
ACT
PP
ACT
R
A formal CD description of 'John gave the man a book'
Primitive acts of conceptual dependency (Schank and Abelson 1977: 12-14) are essentially
acts of 'transfer' and/or 'mutation' : transfer of physical objects, transfer of information,
transfer (application) of force.
Primitive acts or
'semantic
relations'
ATRANS
PTRANS
PROPEL
MOVE
GRASP
INGEST
EXPEL
MTRANS
MBUILD
SPEAK
ATTEND
Elaboration
Transfer of an abstract relationship such as possession
ownership or control
Transfer of the physical location of an object
Application of a physical force to an object
Movement of a body part of an animal by that animal
Grasping of an object by an actor
Taking in of an object by an animal to the inside of that animal
Expulsion of an object from the object of an animal into the
physical world
Transfer of mental information between animals or within an
animal
Construction by an animal of new information of old information
Actions of producing sounds
Action of attending or focusing a sense organ towards a stimulus
Schank's Primitive Conceptual Categories: 'Semantic features' A second set of building
blocks is the set of allowable dependencies among the conceptualisations described in a
sentence. There are four primitive conceptual categories, from which a dependency structure
could be built:
ACT
PP
AA
PA
aiders)
Actions
Objects (picture producers)
modifiers of actions (action aiders)
Modifiers of objects (or PP's) (picture
44
Schanks's Conceptual 'Tenses' Conceptualisations representing events can be modified in a
variety of ways to supply information normally indicated in language by the tense, mood or
aspect of a verb form:
p
f
t
ts
tf
k
?
/
nil Present
delta
c
Past
Future
Transition
Start Transition
Finished Transition
Continuing
Interrogative
Negative
Timeless
Conditional
Advantages of using Conceptual Dependency Grammar for representing knowledge
and reasoning with a CD knowledge base:
1. The organisation of knowledge in terms of primitives (or 'primitive acts') leads to fewer
inference rules
ACTions
Transfer
{Give}
{Take}
ACT
{Steal}
ATRANS
{Donate}
{Borrow}
2. Many inferences are already contained in the representation itself
3. The initial structure that is built to represent the information contained in one sentence will
have holes in it that have to be filled in: holes which will serve as attention focusers for
subsequent sentences.
Semantic Nets and Conceptual Dependency Representation: A comparison
Semantic Nets only provide a structure into which nodes representing information can be
placed. Conceptual Dependency representation, on the other hand, provides both a structure
and a specific set of primitives out of which representations of particular pieces of
information can be constructed.
5.3
'Knowledge lines', 'Nemes' and 'Nomes'
45
In his wide ranging discourse on language, meaning, knowledge, mind, evolution, heritage,
intention and psychology, Minsky has proposed a 'society of mind' scheme - each mind is
made of many smaller processes, and that it is possible that 'you can build a mind from many
little parts, each mindless by itself' (1985:17). This co-operative/distributed ensemble of
individually non-intelligent 'agents' somehow or the other produces 'intelligent behaviour.
These 'agents' perform perceptive, motor and cognitive tasks: agents which see, hear, touch,
smell, speak; agents which grasp, balance, ingest, move; and agents which organise memories,
represent have linguistic 'skills', are capable of 3D vision etc.
There are points in Minsky's discourse where 'no ordinary word seems satisfactory, and I had
to invent new terms or assign new meanings to old ones'. The theory emphasises that in order
to avoid circular reasoning to 'explain the mind', one must rely 'terms of things that have no
thoughts or feelings of their own': the "agents" that compose our minds' (1985:18).
Minsky appears to propound, or perhaps more accurately, subscribes to, a 'relativistic theory of
meaning': 'we'll take the view that nothing can mean by itself, but only in relation to whatever
other meanings we already know'. This 'relativity of meaning' takes a very polysemous turn
when Minsky emphasises that 'the secret of what anything means to us depends on how we've
connected it to all the other things we know. That is why it's almost always wrong to seek the
"real meaning" of anything. A thing with just one meaning has scarcely any meaning at all'!
(Minsky 1985:64).
Minsky argues that human memories are organised in two 'bands'. The lower and the upper
bands: the former deals with 'structural' details of 'things' and the latter with the 'functional'
aspects of 'things'. (There is a considerable use of near-synonyms by Minsky, he
interchangeably uses the terms bands, levels, fringes etc.). The relationship between the
structures and functions of 'things' is central to Minsky's argument: 'What use would thinking
be at all, unless we could relate each thing's details to our plans and intentions'. He then goes
on to elaborate this argument by using homonyms as an example: 'What tools would you use,
when building your house, to saw, and clamp and glue your wood?' The homonyms reply
'saw', 'clamp', and 'glue', for Minsky, is rooted in a mind phenomenon, which is facilitated by
the agency of language, i.e. the evocation of 'corresponding acts' in response to
hearing/reading 'names': 'Behold the wondrous force of the [homonyms] "meanings": no
sooner do we hear the noun form of a word than our agents [of mind] strain to perform the
acts that correspond to it as a verb'. This 'wondrous force', or 'evocationary force' manifests
itself in this linkage between 'nouns' and 'verbs' [Minsky 1985:88].
46
For Minsky the whole notion of hearing the meanings of unfamiliar words relates to the
rebuilding of signs and structures in the minds of a writer/speaker - their 'thoughts - into the
mind of a reader/hearer. This rebuilding exercise involves the use of materials already in the
mind of the reader/hearer. Therefore, the unfamiliar word, and by implication the signs and
structures, are incorporated amongst 'connections among and across fragments of the great
interlocking networks, ever growing and changing connections and constraints amongst our
agencies [of mind]' (Minsky 1985:181). Qua the relativity of meaning.
This very personal, contextualized, functional view of language is further elaborated by
Minsky 'why do we find it so hard to explain the meanings of things? Because what
something thing "means" depends upon each person's state of mind'. (1985:192).
We now examine three types of agents in Minsky's discussion, which we believe are the basic
building blocks of his 'semantic theory', the semantic primitives: The K-lines, nemes and
nomes.
First the K[nowledge]-lines, a wire-like structure that attaches itself to whichever mental
agents are active when you solve a problem or have a good idea (1985:82). K-lines are
variously referred to as a 'host of agents' (1985:60) and as a 'theory of memory' (1985:82) or
more precisely 'the theory that certain kinds of memories are based on turning on sets of
agents that reactivate one's previous mental state'. (1985:329). K-lines are really used as a
superordinate term by Minsky and he also uses the K-lines in the description of nemes.
Second, the neme, 'an agent whose output represents a fragment of an idea or state of mind'.
The 'context' within which a typical agent works is largely determined by the activity of the
nemes that reach it' (1985:330). According to Minsky, 'the neme concept emerged in 1977
(under the term C-lines)'. Minsky distinguishes between two types (?) of nemes: polyneme
and microneme.
A polyneme is 'a single agent which sends messages to several different agencies'. For
example, 'your word-agent for the word "apple" must be a polyneme because it sets your
agencies for colour, shape, [material] and size into unrelated states that represent the
independent properties of being 'red-coloured, medium-sized, a plant material and ball
shaped'. (1985:200).
Micronemes are agents which provide 'those inner mental context clues that shade our mind',
and are envisioned as 'K-lines that reach into many agencies which widespread effects on the
arousal and suppression of other agents - including other micronemes'. Minsky has used a
47
number of semantic features (material: animate/inanimate; natural/artificial; idea; actual, or
shape: hardness; softness; density; flexibility; strength), semantic relationships (including
location: office; home; vehicle; etc.) and autonomous relationships (like co-operation/conflict
and negotiation/confrontation), semantic fields (colour, taste, texture) and semantic roles
(activity: hunting, gambling, entertaining) (1985:211). Micronemes are agents involved at a
relatively [detailed] low level.
Third, the agent 'nome', whose outputs affect an agency in a predetermined manner. [.....]; an
agent whose effect depends more on genetic architecture than on learning from experience'
(1985:330). There are three related subordinate terms used by Minsky here: pronomes,
isonomes and paranomes. Again it appears that pronome is used as a superordinate term, in
that, Minsky defines isonomes and paranomes as a kind of pronomes with specific function.
Pronomes are 'pronounlike devices to exploit whatever mental activities have already been
aroused, to interlink the thoughts already active in the mind' and are involved in 'processes we
use to make both verbal and nonverbal "chains of reasoning"' (1985:217-218). 'Pronomes',
according to Minsky, 'are associated with a particular "role" or aspect of representation'. For
instance, the 'roles' Minsky uses for illustrative purposes includes 'action, actor, cause,
destination, difference, instrument, method, motive, object, obstacle, origin, place, recipient,
time, trajectory, and, vehicle'. Pronomes, 'are temporary K-lines. They are short term
memories.', as compared to polynemes which are 'permanent K-lines. They are long term
memories.' (1985:226).
Isonomes are agents 'which have a similar built -in effect on each of its recipients' and thus
'applies the same ideas to many different things at once.' Compared to polynemes, which
'learn to arouse many different processes at once', the isonomes 'draw their power from
exploiting abilities that are already common to many agencies.' (1985:227).
Paranomes are pronomes which can operate in several different realms at once or 'parallel
pronomes'. The existence of paranomes will help to explain why 'many of our higher level
conceptual-frames are really parallel arrays of analogous frames, each active in a different
realm' (Minsky 1985:294).
A comparison of Minsky's terminology with "computer science" terminology.
48
Minsky term
Agent
B-brain
Exploit an agent
Isonome
K-line
Level bands
Microneme
Neme
Nome
Paranome
Polyneme
Pronome
Script
Short term memory
agent
Stimulus
Transframing
Uniframing
6.
Computer science term
Function; subroutine; module
Operating system; supervisory program
Call a subroutine
Variable symbol implemented as a process
Interrupt vector; branch table
"Ports" that restrict communications, e.g. between address spaces
Microinstruction
Process that builds a record within a data structure of binds a value to a
variable
Function returning a coded value
A nome which returns a value that, when transmitted to multiple
recipients, has the same effect on each of them
A process which returns a value that, when transmitted to multiple
recipients, has different effects on each of them
Local variable to which a value has been bound; short term memory
Procedure; algorithm
Register file implemented as a process
Argument list
Linking by communicating; metaphor
Set intersection
Conclusions
The discussion presented in this paper is essentially a survey of how the term semantic
primitives is used in a range of related disciplines: the fact that the term semantics and
semantic primitives are polysemous, within the confines of individual disciplines like
linguistic semantics, cognitive psychology, special language studies, and artificial
intelligence, is perhaps an indication of how broad these terms are. But an interdisciplinary
study like ours indicates the extensive overlaps in meaning across disciplines that can be
exploited in the studies of information processing and learning by humans and by machines.
The mere fact that there are key semantic relations, that of hyponymy and mereonymy, that in
themselves are related to human perception (cf. extant taxonomies in societies, dimensions of
physical objects, body parts and their organisation), to natural language and learning in
general, should make it obvious to workers in diverse disciplines that input from other
disciplines is of crucial important. This is particularly true of workers in artificial
intelligence where intuition, dressed sometimes in logic and at other times in neurobiology,
and hypotheses, presented as synthesis of work in cognition and epistemology, play a major
role in theory formation and systems implementation. The lax use of terms used in
semantics, and almost ad-hoc creation and use of neologisms, in artificial intelligence does
impede the progress in the discipline.
49
References
Aitchison, Jean. (1987). Words in the Mind: An Introduction to the Mental Lexicon. Oxford:
Blackwell
Aitchison, Jean. (1990). Reviews: ‘Cruse. D. 1986. Lexical Semantics’. International
Journal of Lexicography 3/2 (Summer 1990). pp147-149.
Calzolari, Nicoletta (1988). ‘The dictionary and the thesaurus can be combined’. In (Ed)
Martha Walton Evens. pp 75-96.
Carnap, Rudolf. (1937). The Logical Syntax of Science. London: Routledge & Kegan Paul
Limited. (Translated by Amethe Smeaton - Countess von Zeppelin. Reprinted Edition 1971).
Cruse, David. A. (1986). Lexical Semantics. Cambridge: Cambridge University Press.
Dehn, Natalie & Schanck, Roger. (1982). ‘Artificial and human intelligence’. In (Ed) Robert
J. Sternberg (1982). pp. 352-391.
Dixon, Robert, M. W. (1991). Words of Our Country. St. Lucia, Queensland: Queensland
University Press.
Dixon, Robert, M. W. (1982). Where Have All the Adjectives Gone? and other essays in
Syntax and Semantics. Berlin: Mouton Publishers.
Evens, Martha Walton (1988). ‘Introduction’. In (Ed) Martha Walton Evens. pp 1-37.
Estes, William K. (1982). ‘Learning, memory and intelligence’. In (Ed) Robert J. Sternberg
(1982). pp. 170-224.
Evens, Martha Walton (Ed). (1988). Relational Models of the lexicon:
Representing
knowledge in semantic networks. Cambridge: Cambridge Univ. Press.
Glass, Andrew Lewis & Holyoak, Keith James (1986). Cognition. London: McGraw-Hill
Book Company. (Second Edition).
Jackendoff, Ray. (1983). Semantics and Cognition. Cambridge, MASS.: The MIT Press.
50
Jackendoff, Ray. (1990). Semantic Structures. Cambridge, MASS.: The MIT Press.
Kempson, Ruth M. (1984). Semantic Theory. Cambridge: Cambridge University Press.
Lehrer, Adrienne (1979).
Holland.
‘Semantic fields and lexical structure’.
Amsterdam:
North
Lyons, John. (1977). Semantics. Cambridge: Cambridge University Press.
Minsky, Marvin. (1985) The Society of Mind. New York: Simon and Schuster.
Palmer, F. R. (1981). Semantics. Second Edition. Cambridge: Cambridge University Press.
Popper, Karl R. (1981). Objective Knowledge - An Evolutionary Approach (Revised Edition).
Oxford: Clarendon Press.
Richens, R H (1959) 'Tigris and Euphrates - A Comparison between Human and Machine
Translation'. Proc. of an NPL Symposium 24-27 Nov 1958. London: HMSO. pp 281-307
Sloman, Aaron. (1978). The Computer Revolution in Philosophy -- Philosophy, Science and
Mind. Hassocks (Sussex): The Harvester Press Limited.
Smith, Edward E. & Medin, Douglas L. (1981). Categories and Concepts. Cambridge,
MASS: Harvard University Press.
Sternberg, Robert J. (Ed). (1982). Handbook of human intelligence. Cambridge: Cambridge
University Press.
Thompson, Sandra A. (1989) ‘A discourse approach to the cross-linguistic category
“Adjective”’. In (Eds.) Roberta Corrigan, Fred Eckman and Michael Noonan. Linguistic
Categorization. Amsterdam/Philadelphia: John Benjamins Publishing Company. pp 245265.
Bechtel, Williams. (1988). Philosophy of Mind - An Overview for Cognitive Science.
Hillsdale (NJ): Lawrence Erlbaum Associates
51
- AGENT
- ACT-OF
- PROPERTY-NAME
- LOCATION
- SET-OF
- INHABITANT-OF
- FOLLOWER-OF
Figure 6.1: Template for SUBSTANCE nouns:
FUNCTION: USED-FOR:
USED-IN:
USED-AS:
USED-BY:
PROPERTY:NATURE:
STRUCTURE:
ORIGIN:
STATE:
TASTE:
SMELL:
COLOUR:
SHAPE:
ASPECT:
LACKING:
SIMILAR-TO:
CONSTITUENCY: CONSTITUTED-BY:MAIN:
MUCH:
CONSTITUTED-OF:MAIN:
SOURCE:
DERIVED-FROM:
PRODUCED-BY:
PRODUCED-BY-MEANS-OF:
LOCATION:
CAUSE-OF:
NAME:
52
2
A Consensus Framework for Classifying Semantic Relations
From the point of view of building an information resource, like a lexical database
management system or a term bank, it is important to use a framework for the study of
meaning which has a wide measure of support. The inputs (or queries) to and outputs from
the data base will be chosen and interpreted respectively on the basis of this measure of
support: the user expectation related to the inputs and outputs depends on a common
consensus about ‘meaning’. The linguists, the system builders and the lexical data base endusers must have a common understanding of the notion of ‘meaning’.
Theories of linguistic semantics and, to a degree, theories of philosophical semantics, are
essentially attempts to model aspects of human language and human behaviour. The
considerations of the language or the behaviour implicitly or explicitly subscribe to
influences from a variety of different disciplines: either at the level of an individual, for
instance, studies in psycholinguistics and neurolinguistics, or across languages, for example
explorations in contrastive linguistics, or at the level of a society as a whole, for instance, the
work of anthropologists involved in creating archives of a people. The terminology used in
the individual linguistic semantic theory, and, perhaps, equally importantly, the terminology
not used in the given theory, is an indicator of the theorists’ view of the world, view of
human psychology, and view of language. Each theorist provides insights into the semantic
relations and semantic features by using examples, either contrived or drawn from sentences
uttered by real people, to test his or her theory and to disprove other theories. In order to
build an information resource for inter-relating linguistic units, like words, terms or
sentences, it is essential that for specifying and designing that (computer-based) resource one
uses a description of semantic relations which has degree of international recognition and for
which there is a measure of consensus on the terminology used.
We have used the publications of the International Standards Organisation (ISO), particularly
ISO 2788-1986 (Documentation - Guidelines for the Establishment and Development of
Monolingual Thesauri), to organise and represent the work of a number of linguists,
semanticists, terminologists, and workers in Artificial Intelligence. Our collage is largely
restricted to semantic relations. We have noted that the various discussions of semantic
relations summarised in the collage are characterised by a variety of terminology to describe
the same relations. In an effort to produce a clearer picture of the various ‘semantic
relations’ and to facilitate comparison across such seemingly disparate areas as lexicography
and knowledge engineering, we have attempted to classify the relations in the collage
according to an ISO classification. This classification has been adopted because we believe
that the classes of relations identified by ISO (equivalence, hierarchical, and associative) are
53
linguistically and epistemologically more relevant for the task of building an information
resource like a terminology data bank.
ISO 2788 identifies three classes of basic inter-term relationship: a) the equivalence
relationship; b) the hierarchical relationship; c) the associative relationship. These
relations are defined and exemplified in the standard as follows:
2.1 The equivalence relationship
According to ISO 2788 this is the relationship between preferred and non-preferred terms
where two or more terms are regarded, for indexing purposes, as referring to the same
concept. For instance, consider the terms: birds and aves. This general relationship covers
two types of term:
Synonyms: are terms whose meanings can be regarded as the same in a wide range of
contexts, so that they are virtually interchangeable. Various types of synonym are
encountered in practice. The following list, which ISO claims is not intended to be
exhaustive, indicates some of the more common classes of synonyms encountered in
practice:
a)
b)
c)
d)
e)
f)
g)
h)
i)
terms of different linguistic origin:
polyglot multilingual
popular names and scientific names:
penguins
sphenisciformes
common nouns and trade names: vacuum flasks thermos flasks
variant names for new concepts: hovercraft
air cushion vehicles
current or favoured terms versus
outdated or deprecated terms: radio
wireless
variant spellings, including stem
variants and irregular plurals
Romania
Rumania
Roumania
terms originating from different
cultures sharing a common language: flats
apartments
abbreviations and full names:
PVC
polyvinyl chloride
the factored and unfactored form
of a compound:
coal + mining
coal mining
Quasi-synonyms: are terms whose meanings are generally regarded as different in ordinary
usage, but they are treated as though they are synonyms for indexing purposes. These terms
frequently represent points on a continuum:
wetness
dryness
2.2 The hierarchical relationship
According to ISO 2788 this relationship most distinguishes a systematic thesaurus from an
unstructured list of terms, for example an alphabetical glossary or dictionary. It is based on
degrees or levels of superordination and subordination, where the superordinate term
represents a class or whole, and subordinate terms refer to its members or parts:
54
airlines
transport services
This general relationship covers three logically different situations distinguished as follows:
a) the generic relationship; b) the hierarchical whole-part relationship; c) the instance
relationship. Each of these leads to hierarchies which are amenable to a logic test through
reference to the basic types of concept represented by the terms.
The generic relationship: This relationship identifies the link between a class or category
parrots
and its members or species: birds
The hierarchical whole-part relationship: This relationship covers a limited range of
situations where the name of a part implies the name of its possessing whole in any context.
The terms can be organised as a hierarchy, the name of the whole serving as the
superordinate term, and the name of the part as the subordinate term. This applies to four
main classes of terms:
i). systems and organs of the body:
circulatory system Æ
cardio-vascular system→arteries→veins
ii). geographical locations:
Canada→Ontario→Ottawa→Toronto
iii). disciplines or fields of discourse:science→biology→botany or zoology
iv). hierarchical social structures:
armies→corps→division→battalions
The instance relationship identifies the link between a general category of things or events,
expressed by a common noun, and an individual instance of that category, the instance then
forming a class-of-one which is represented by a proper name.
mountain regions:
Alps or Himalayas
2.3 The associative relationship
This relationship is easier to define in terms of negative rather than positive characteristics.
It covers relationships between pairs of terms which are not members of an equivalence set,
nor can they be organised as a hierarchy in which one term is subordinated to another, yet
they are mentally associated to such an extent that the link between them should be made
explicit in the thesaurus, on the grounds that it would reveal alternative terms which might be
used for indexing or retrieval. Two kinds of term can be linked by the associative
relationship: those that belong to the same category; and those belonging to different
categories.
Terms belonging to the same category relate siblings with overlapping meanings, such
as “ships” and “boats”, where each of the terms is amenable to an exact definition,
(consequently they do not form an equivalence set), yet they are sometimes used loosely and
55
almost interchangeably, so that the user seeking documents on one of the terms should be
reminded of the other.
Terms belonging to different categories: Many grounds can be established for
associating terms belonging to different categories (i.e. they refer to different conceptual
types), while satisfying the requirements that one of the terms should be strongly implied by
the other. The following groups are offered only as representative examples of typical
relational situations encountered in practice:
i). a field of study and the objs. or phenomena studied: forestry forests
ii). an operation or process and its agent or instrument: temperature control
thermostats
iii). an action and the product of the action: weaving
cloth
iv). an action and its patient:
harvesting
crops
v). concepts related to their properties:
poisons
toxicity
vi). concepts related to their origins:
Dutch
Netherlands
vii). concepts linked by causal dependence
bereavement
death
viii). a thing and its counter agent:
plants
herbicides
ix). a concept and its unit of measurement:
electric current ampere
x). syncategorematic phrases and their
model ships
ships
embedded nouns
56
The functional component
The first schema views the part as a functioning unit in a whole, such as an organ of the body
or an engine in a car. The part, in this sense, contributes to the whole, not just as a structural
unit but as essential to the purposeful activity of the whole. Figure 1 illustrates this sense.
Figure 1. Functional component of a whole
The segmented whole
This schema emphasises the whole which is divided into pieces like a pie, as shown in Figure
2. This conception of the part-whole relation implies the removability of the part or the
divisibility of the whole. Some sense of entatitivity is attributed to the part, even though it
may be removed from its whole.
Figure 2. The segmented whole
57
Collections and members
In this sense the relationship of part to whole is the relationship of member to collection or
element to set. In its simplest form this schema denotes a physical collection or aggregate of
objects that are spatially close together, but have no particular structural organisation, as
shown in Figure 3.
Figure 3. Member of a collection
Sets and subsets
This model of part-whole is based on the more primitive one of member and collection. The
set A is a subset of B if and only if every member of A is also a member of B. Figure 4A
gives a simple illustration of a subset of a set. This notion of set is basic to the abstractions
of modern mathematics.
Figure 4A. A subset of a set
58
This is an interesting piece picked up from a lexicography
book, find the source.
Even a cursory survey of the varieties of metascience bears out the claim that schools of
metascience have been proliferating, not quite at the rate of one per person, but at the rate of
one per principle. A shopping list of schools of metascience includes the following: logical
empirical metascience [Carnap 1967]; general cognitive metascience [Kuhn 1970, 1977];
empirical cognitive metascience [Mitroff 1974]; descriptive, evolutionary, cognitive
metascience [Campbell 1977]; naive cognitive metascience [McCloskey 1983];
falsificationist metascience [Popper 1975]; methodological metascience [Lakatos 1970];
sociological metascience [Bloor 1976]; ethnomethodological metascience [Gilbert and
Mulkay 1984]; semiotic metascience [Gopnik 1977]; dialectical hermeneutic metascience
[Habermas 1970]; documentational metascience [Garfield 1979]; and even anarchistic
metascience [Feyerabend 1972]. If one also includes the human sciences as the object of
inquiry of metascience then there are also such schools as deconstructionist metascience
[Derrida 1977] and discursive metascience [Foucault 1972].
59
60