PLANNING COHERENT 
MULTISENTENTIAL TEXT 
Eduard H. Hovy 
USC/Information Sciences Institute 
4676 Admiralty Way, Suite 1001 
Marina del Rey, CA 90292-6695, U.S.A. 
HOVY~VAXA.ISI.EDU 
Abstract 
Though most text generators are capable of sim- 
ply stringing together more than one sentence, 
they cannot determine which order will ensure 
a coherent paragraph. A paragraph is coherent 
when the information in successive sentences fol- 
lows some pattern of inference or of knowledge 
with which the hearer is familiar. To signal such 
inferences, speakers usually use relations that llnk 
successive sentences in fixed ways. A set of 20 
relations that span most of what people usually 
say in English is proposed in the Rhetorical Struc- 
ture Theory of Mann and Thompson. This paper 
describes the formalization of these relations and 
their use in a prototype text planner that struc- 
tures input elements into coherent paragraphs. 
1 The Problem of Coherence 
The example texts in this paper are generated 
by Penman, a systemic grammar-based genera- 
tor with larger coverage than probably any other 
existing text generator. Penman was developed 
at ISI (see \[Mann & Matthiessen 831, \[Mann 831, 
\[Matthiessen 84\]). The input to Penman is pro- 
duced by PEA (Programming Enhancement Ad- 
visor; see \[Moore 87\]), a program that inspects a 
user's LISP program and suggests enhancements. 
PEA is being developed to interact with the user 
in order to answer his or her questions about the 
suggested enhancements. Its theoretical focus is 
the production of explanations over extended in- 
teractions in ways that are superior to the simple 
goal-tree traversal of systems such as TYRESIAS 
(\[Davis 76\]) and MYCIN (\[Shortliffe 76\]). 
Supported by DARPA contract MDAg03 81 C0~5. 
In answer to the question how does the system 
enhance a program~, the following text (not gen- 
erated by Penman) is not satisfactory: 
(a). The system performs the enhance- 
ment. Before *hat, the system resolves 
conficts. First, the system asks the 
user to tell Jt the characteristic of the 
program to be enhanced. The system 
app//es transformations to the program. 
/t confrms the enhancement with the 
user. It scans the program in order to 
find opportunities to apply transfarma- 
tions to the program. 
... because you have to work too hard to make 
sense of it. In contrast, using the same propo- 
sitions (now rearranged and linked with appro- 
priate connectives), paragraph (b) (generated by 
Penman) is far easier to understand: 
(b). The system as/ca ~he user to tell 
it the characteristic of the program to 
be enhanced. Then the system applies 
transformations to the program. In par- 
ticular, the system scans the program 
in order to ~nd opportunities to apply 
transformations to the program. Then 
the system resolves contlicts. It con~rms 
the enhancement with the user. Fina//y, 
it performs the enhancement. 
Clearly, you do not get coherent text simply by 
stringing together sentences, even if they are re- 
lated -- note especially the underlined text in (b) 
and its corresponding three propositions in (a). 
The goal of this paper is to describe a method of 
planning paragraphs to be coherent while avoiding 
unintended spurious effects that result from the 
juxtaposition of unrelated pieces of text. 
163 
2 Text Structuring 
This planning work, which can be called tezt 
siructuring, must obviously be clone before the 
actual generating of language can begin. Text 
structuring is one of a number of pre-generation 
text planning tasks. For some of the other tasks 
Penman has special-purpose domain-specific solu- 
tions. They include: 
• aggregation: determining, for input ele- 
ments, the appropriate level of detail (see 
\[Hovy 87\]), the scoping of sentences, and the 
use of connectives 
• reference: determining appropriate ways of 
referring to items (see \[Appelt 87a, 87b\]) 
• hypotheticals: determining the introduc- 
tion, scope, and closing of hypothesis contexts 
(spans of text in which some values are as- 
sumed, as in air you want to go to the game, 
then ... ~) 
The problem of text coherence can be character- 
ized in specific terms as follows. Assuming that in- 
put elements are sentence- or clause-sized chunks 
of representation, the permutation set of the input 
elements defines the space of possible paragraphs. 
A simplistic, brute-force way to achieve coherent 
text would be to search this space and pick out 
the coherent paragraphs. This search would be 
factorlally expensive. For example, in paragraph 
(b) above, the 7 input clusters received from PEA 
provide 7! ---- 5,040 candidate paragraphs. How- 
ever, by utilizing the constraints imposed by co- 
herence, one can formulate operators that guide 
the search and significantly limit the search to a 
manageable size. In the example, the operators 
described below produced only 3 candidate para- 
graphs. Then, from this set of remaining candi- 
dates, the best paragraph can be found by apply- 
ing a relatively simple evaluation metric. 
The contention of this paper is that, exercis- 
ing proper care, the coherence relations that hold 
between successive pieces of text can be formu- 
lated as the abovementioned search operators and 
used in a hierarchical-expanslon planner to limit 
the search and to produce structures describing 
the coherent paragraphs. 
The illustrate this contention, the Penman text 
structurer is a simplified top-down planner (as de- 
scribed first by \[Sacerdoti 77\]). It uses a formal- 
ized version of the relations of Rhetorical Struc- 
ture Theory (see immediately below) as plans. Its 
output is one (or more) tree(s) that describe the 
structure(s) of coherent paragraphs built from the 
input elements. Input elements are the leaves of 
the tree(s); they are sent to the Penman generator . 
to be transformed into sentences. 
3 Previous Approaches 
The heart of the problem is obviously coherence. 
Coherent text can be defined as text in which the 
hearer knows how each part of the text relates to 
the whole; i.e., (a) the hearer knows why it is said, 
and (b) the hearer can relate the semantics of each 
part to a. single overarching framework. 
In 1978, Hobhs (\[Hobhs 78, 79, 82\]) recognized 
that in coherent text successive pieces of text are 
related in a specified set of ways. He produced 
a set of relations organised into four categories, 
which he postulated as the four types of phenom- 
ena that occur during conversation. His argument, 
unfortunately, contains a number of shortcomings; 
not only is the categorization not well-motivated, 
but the llst of relations is incomplete. 
In her thesis work, McKeown took a different 
approach (\[McKeown 82\]). She defined a set of 
relatively static schemas that represent the struc- 
ture of stereotypical paragraphs for describing ob- 
jects. In essence, these schemas are paragraph 
templates; coherence is enforced by the correct 
nesting and 6\]llng.in of templates. No explicit the- 
ory of coherence was offered. 
Mann and Thompson, after a wide-ranging 
study involving hundreds of paragraphs, proposed 
that a set of 20 relations suffice to represent the 
relations that hold within the texts that normally 
occur in English (\[Mann & Thompson 87, 86, 
83\]). These relations, called RST (rhetorical struc- 
ture theory), are used recursively; the assumption 
(never explicitly stated) is that a paragraph is only 
coherent if all its parts can eventually be made to 
fit into one overarching relation. The enterprise 
was completely descriptive; no formal definition 
of the relations or justification for their complete- 
ness were given. However, the relations do include 
most of Hobbs's relations and support McKeown's 
schemas. 
A number of similar descriptions exist. The de- 
scription of how parts of purposive text can re- 
late goes back at least to Aristotle (\[Aristotle 54 D. 
Both Grimes and Shepherd categorize typical in- 
tersentential relations (\[(\]rimes 75\] and \[Shepherd 
26\]). Hovy (\[Hovy 86\]) describes a program that 
uses some relations to slant text. 
164 
4 Formalizing RST Relations 
As defined by Mann and Thompson, RST rela- 
tions hold between two successive pieces of text 
(at the lowest level, between two clauses; at the 
highest level, between two parts that make up 
a paragraph} 1. Therefore, each relation has two 
parts, a aucle~ and a satell~te. To determine the 
applicability of the relation, each part has a set 
of constraints on the entities that can be related. 
Relations may also have requirements on the com- 
bination of the two parts. In addition, each rela- 
tion has an effect field, which is intended to denote 
the conditions which the speaker is attempting to 
achieve. 
In formalizing these relations and using them 
generatively to plan paragraphs, rather than ana- 
lytically to describe paragraph structure, a shift of 
focus is required. Relations must be seen as plans 
the operators that guide the search through the 
permutation space. The nucleus and satellite con- 
straints become requirements that must be met by 
any piece of text before it can be used in the re- 
lation (i.e., before it can be coherently juxtaposed 
with the preceding text}. The effect field contains 
a description of the intended effect of the relation 
(i.e., the goal that the plan achieves, if properly 
executed}. Since the goals in generation are com- 
municative, the intended effect must be seen as 
the inferences that the speaker is licensed to make 
about the bearer's knowledge after the successful 
completion of the relation. 
Since the relations are used as plans~ and since 
their satellite and nucleus constraints must be re- 
formulated as subgoais to the structurer, these 
constraints are best represented in terms of the 
communicative intent of the speaker. That is, they 
are best represented in terms of what the hearer 
will know -- i.e., what inferences the hearer would 
run -- upon being told the nucleus or satellite 
filler. 
As it turns out, suitable terms for this purpose 
are provided by the formal theory of rational inter- 
action currently being developed by, among oth- 
ers, Cohen, Levesque, and Perrault. For example, 
in ICohen ~z Levesque 851, Cohen and Levesque 
present a proof that the indirect speech act of re- 
questing can be derived from the following bask 
modal operators 
• (BEL x p) -- p follows from x's beliefs 
1This is not strictly true; a small number of relations, 
such as Seqtlence, relate more than two pieces of text. 
However, for ease of use, they have been implemented as 
binary relations in the structurer. 
• (BMB x y p) -- p follows from x's beliefs 
about what x and y mutually believe 
• (GOAL x p) -- p follows from x's goals 
• (.AFTER a p) -- p is true in all courses of 
events after action a 
as well as from a few other operators such as AND 
and OR. They then define suture,ties as, essen- 
tiaUy, speech act operators with activating condi- 
tious (g~tes) and e~ectz. These summaries closely 
resemble, in structure, the RST plans described 
here, with gates corresponding to satellite and nu- 
cleus constraints and effects to intended effects. 
5 An Example 
The RST relation Purpose expresses the relation 
between an action and its intended result: 
= Pro.pose 
Nucleus Constraintsz 
1. (BMB S H (ACTION ?act-l)) 
2. (BMB S H (ACTOR ?act-1 ?agt-1)) 
Satellite Constraintsz 
1. (BMB S H (STATE ?state-l)) 
2. (BMB S H (GOAL ?a~-I ?state-l)) s. (B~ S H (RESULT Zact-1 ?~t-2)) 
4. (BMB S H (OBJ ?act-2 ?state-I)) 
Intended EEectss 
1. (BMB S H (BEL ?ag~-I (RESULT ?act-1 ?state-l))) 
2. (BMB S H (PURPOSE ?act-I ?state-l)) 
For example, when used to produce the sentence 
The system scans the program in order to find op- 
portunltJes to apply ~ansformatlons to t~e pro- 
gram, this relation is instantiated as 
I:~I3UL'pO|6 
Nucleus Coustraints- 
I. (B~m S H (ACTION SCA~-I)i 
The program k scanned 
2. (BMB S H (ACTOR SCAN-I SYS-I}) 
The system scans it 
Satellite Constraints: 
1. (BMB S H (STATE oee-1)) 
Opportunities to apply transformations exkt 
2. (BMB S H (GOAL SYS-10PP-1)) 
The system =wants" to find them 
3. (BMB S H (RESULT SCAN-1 FIND-I)) 
Scanning wil/result; in findlng 
4. (BMB S H (OBJ FIND-10PP-1)) 
the opportunities 
Intended Effects: 
1. (BMB S H (BEL SYS-1 (RESULT 
SCAN-10PP-1})) 
The system ~believes = that scanning 
will disclose the opportunities 
2. (BMB S H (PURPOSE SCAN-10PP-I)) 
This is the purpose of the scanning 
15S 
• /SRTELL.IrTE_SEQUEttCE~qTELL~TE-,(YHPUTREC with (P3)=' (~) 
SRTELL~TE--SEQUEtlCI~ I'OJCL£US--<IrlPUTREC ,A'lth (C2 f14) * (~ 
%rlUCLEUS--<Ir(PUTREC vlt.h (R1 C4)) ~P-) ( ,~I'ELLI T E-- SE OUEtICE/t 
J ~ , /SRTELL'II'E--('rltPUTREC u4th (FI KS)* (~) 
/SATELLITE--ELROORRTIO~ " tNUCLEUS--PURPOS%NUCLEUS--¢IttPUTREC v, th (S2) * Co) 
S~QUEHC~ I=I'tt,ICLEUS-. <ZHPUTREC utth (R2) • ~ ~) 
ttUCL£US--(IHPUTRgC vlth (RI P4 E6))~ 
Figure 1: Paragraph Structure ~ree 
The elements SCAN-l, OPP-1, etc., are part 
of a network provided to the Penman structurer 
by PEA. These elements are defined as propo- 
sitions in a property-inheritance network of the 
usual kind written in NIKL (\[Schmolze & Lipkis 
83\], \[Kaczmarek et aL 86\]), a descendant of KL- 
ONE (\[Brachman 78\]). Some input for this exam- 
ple sentence is: 
(PEA-SYST~4 SYS-I) " (OPPORTUNITY OPP-I) 
(PROGRAM PROG-I) (EHABL~4ENT ENAB-S) 
(SCAN SCAN-I) (DOMAIN F~-S OPP-I) 
(ACTOR SCAN-I &",'S-l) (RANGE EN)3-S APPLY-3) 
(OBJ SCAN-I PROG-I) (APPLY APPLY-3) 
(RESULT SCAN-1-FIND-l) (ACTOR APPLY-3 SYS-1) 
(FIND FIND-I) (OBJ APPLY-S TKANS-2) 
(ACTOR FI~)-I SYS-I) (RZCIP APPLY-3 PROG-1) 
(OBJ FIND-I OPP-I) (TRANSFORMATION TRANS-2) 
The relations are used as plans; their intended 
effects are interpreted as the goals they achieve. 
In other words, in order to bring about the state 
in which both speaker and hearer know that OPP-1 
is the purpose of SCAN-I (and know that they both 
know it, etc.), the structurer uses Purpose as a 
plan and tries to satisfy its constraints. 
In this system, constraints and goals are inter- 
changable; for example, in the event that (RESULT 
SCAN-I FIND-I) is believed not known by the 
hearer, satellite constraint 3 of the Purpose re= 
lation simply becomes the goal to achieve (BHB S 
H (RESULT SCAN-I FIND-I)). Similarly, the propo- 
sitions (B~ S H (RESULT SCAN-1 ?ACT-2)) (BMB S 
H (0BJ ?ACT-2 0PP-I)) are interpreted as the goal 
to find some element that could legitimately take 
the place of ?ACT-2. 
In order to enable the relations to nest recur- 
sively, some relations' nucleuses and satellites con- 
taln requirements that specify additional relations, 
such as examples, contrasts, etc. Of course, these 
additional requirements may only be included ff 
such material can coherently follow the content of 
the nucleus or satellite. The question of ordering 
such additional constituents is still under investi- 
gation. The question of whether such additional 
material should be included at all is not addressed; 
the structure," tries to say everything it is given. 
The structurer produces all coherent paragraphs 
(that is, coherent as defined by the relations) that 
satisfy the given goal(s) for any set of input ele- 
ments. For example, paragraph (b) is produced to 
satiny the initial goal (BMB S e (SEQUENCE ASK-1 
?l~E~r)). This goal is produced by PEA, to- 
gether with the appropriate representation ele- 
ments (ASK-1. SCAM-I, etc.) in response to the 
question hoto a~oes ~e system enhance a progr~m~. 
Di~erent initial goals will result in di~erent pars- 
graphs. 
Each paragraph is represented as a tree in which 
branch points are RST relations and leaves are 
input elements. Figure 1 is the tree for para- 
graph (b). It cont~n, the relations Sequence 
(signalled by "then" and "finally'i, Elaboration 
('in particular'), and Purpose ('in order to'). 
In the corresponding paragraph produced by Pen- 
man, the relations' characteristic words or phrases 
(boldfaced below) appear between the blocks of 
text they relate: 
\[The system asks the user to tell it 
the character~stlc of the program to be 
enhanced.l(6) Then \[the system applies 
transformations to the program.\](b) In 
particular, \[the system scans the pro- 
gram\](c) in order to \[f~nd opportu- 
nitlea to apply ~ranaformations to the 
program.\]{a) Then \[the system resolves 
conflicts.\](e) lit confu'ms the enhance- 
meng with the user.\](/) Finally, \[it per- 
forms the enhancement.\](g) 
166 
i 
I 
input 
update agenda 
get next bud 
expand bud 
grow tree 
H \] 
I 
choose final plan 
RST relations 
sentence 
generator 
Figure 2: Hierarchical Planning Structurer 
6 .... The Structurer 
As stated above, the structurer is a simplified 
top-down hierarchical expansion planner (see Fig- 
ure 2). It operates as follows: given one or more 
communicative goals, it find s RST relations whose 
intended effects match (some of) these goals; it 
then inspects which of the input elements match 
the nucleus and subgoal constraints for each re- 
lation. Unmatched constraints become subgoals 
which are posted on an agenda for the next level 
of planning. The tree can be expanded in either 
depth-first or breadth-first fashion. Eventually, 
the structuring process bottoms out when either: 
(a) all input elements have been used and unsatis- 
fied subgoais remain (in which case the structurer 
could request more input with desired properties 
from the encapsulating system); or (b) all goals 
axe satisfied. If more than one plan (i.e., para. 
graph tree structure) is produced, the results axe 
ordered by preferring trees with the minimum un- 
used number of input elements and the minimum 
number of remaining unsatisfied subgoals. The 
best tree is then traversed in left-to-right order; 
leaves provide input to Penman to be generated 
in English and relations at branch points provide 
typical interclausal relation words or phrases. In 
this way the structurer performs top-down goal re- 
finement clown to the level of the input elements. 
7 Shortcomings and Further 
Work 
This work is also being tested in a completely sep- 
arate domain: the generation of text in a multi- 
media system that answers database queries. Pen- 
man produces the following description of the ship 
Knox (where CTG 070.10 designates a group of 
ships): 
(c). Knox is en route in order to ren- 
denvous with CTG 070.10, arriving in 
Pearl Harbor on 4/24, for port visit until 4~so. 
In this text, each clause (en route, rendezvous, 
arrive, visit) is a separate input element; the 
structurer linked them using the relations Se- 
quence and Purpose (the same Purpose as 
shown above; it is signalled by ~in order toN). 
However, Penman can also be made to produce 
(d). Knox is en route in order to ren- 
dezvous with CJTG 070.10. It w~11 arrive 
in Pearl Harbor on 4/24. It will be on 
port visit until 4/30. 
The problem is clear: how should sentences in 
the paragraph be scoped? At present, avoiding 
any claims about a theory, the structurer can feed 
167 
Penman either extreme: make everything one sen- 
tence, or make each input element a separate sen- 
tence. However, neither extreme is satisfactory; 
as is clear from paragraph (b), ashort" spans of 
text can be linked and "long" ones left separate. 
A simple way to implement this is to count the 
number of leaves under each branch (nucleus or 
satellite) in the paragraph structure tree. 
Another shortcoming is the treatment of input 
elements as indivisible entities. This shortcoming 
is a result of factoring out the problem of aggre- 
gation as a separate text planning task. Chunking 
together input elements (to eliminate detail) or 
taking them apart (to be more detailed) has re- 
ceived scant mention -- see \[Hovy 87\], and for the 
related problem of paraphrase see \[Schank 75\] -- 
but this task should interact with text structur- 
ing in order to provide text that is both optimally 
detailed and coherent. 
At the present time, only about 20~ of the RST 
relations have been formalized to the extent that 
they can be used by the structurer. This formal- 
ization process is di~cult, because it goes hand- 
in-hand with the development of terms with which 
to characterize the relations' goals/constra£uts. 
Though the formalization can never be completely 
finalized -- who can hope to represent something 
like motivation or justification complete with all 
ramifications? -- the hope is that, by having the 
requirements stated in rather basic terms, the re- 
lations will be easily adaptable to any new repre- 
sentation scheme and domain. (It should be noted, 
of course, that, to be useful, these formalizations 
need only be as specific and as detailed as the do- 
m~in model and representation requires.) In ad- 
dition, the availability of a set of communicative 
goals more detailed than just say or ask (for ex- 
ample), should make it easier for programs that 
require output text to interface with the gener- 
ator. This is one focus of current text planning 
work at ISL 
8 Acknowledgments 
For help with Penman, Robert Albano, John Bate- 
man, Bob Kasper, Christian Matthiessen, Lynn 
Poulton, and Richard Whitney. For help with the 
input, Bill Mann and Johanna Moore. For general 
comments, all the above, and Cecile Paris, Stuart 
Shapiro, and Norm Sondheimer. 
9 
1. 
2. 
References 
Appelt, D.E., 1987a. 
A Computational Model of Referring, SRI 
Technical Note 409. 
Appelt, D.E., 1987b. 
Towards a Plan-Based Theory of Referring 
Actions, in Natural Language Generation: 
Recent Advances in Artificial Intelligence, 
Psyclwlogy, and Linguistic8, Kempen, G. 
(ed), (Kluwer Academic Publishers, Boston) 
63-70. 
3. 
4. 
Aristotle, 1954. 
The Rhetoric, in The l~,eto~c and the Po- 
etics of Ar~to~e, W. Rhys Roberts (Pans), 
(Random House, New York). 
Brachman, R.J., 1987. 
A Structural Paradigm for Representing 
Knowledge, Ph.D. dissertation, Harvard Uni- 
versity; also BBN Research Report 3605. 
5. Cohen, P.R. & Levesque, H.J., 1985. 
Speech Acts and Rationality, Proceedings of 
the A CL Conference, Chicago (49-59). 
6. Davis, R., 1976. 
Applications of Meta-Level Knowledge to 
the Constructions, Maintenance, and Use of 
Large Knowledge Bases, Ph.D. dissertation, 
Stanford University. 
7. Grimes, J.E., 1975. 
The Thread of D/~course 
Hague). 
(Mouton, The 
8. Hobbs, J.R., 1978. 
Why is Discourse Coherent?., SRI Technical 
Note 176. 
9. 
10. 
Hobbs, J.R., 1979. 
Coherence and Coreference, in Cognitive Sci- 
ence 3(1), 67-90. 
Hobbs, J.R., 1982. 
Coherence in Discourse, in Strategies for Nat- 
ural Language Processing, Lehnert, W.G. & 
Ringle, M.H. (eds), (Lawrence Erlbaum As- 
sociates, \]:\[HI.dale N J) 223-243. 
11. Hovy, E.H., 1986. 
Putting Affect into Text, Proceedings of 
the Cognitive Science Society Conference, 
Amherst (669-671). 
168 
12. Hovy, E.H., 1987. 
Interpretation in Generation, Proceedings of 
the AAAI Conference, Seattle (545-549). 
13. Kaczmarek, T.S., Bates, R. & Robins, G., 
1986. 
Recent Developments in NIKL, Proceedings 
of the AAAI Conference, Philadelphia (978- 
985). 
14. Mann, W.C., 1983. 
An Overview of the Nigel Text Generation 
Grammar, USC/Information Sciences Insti- 
tute Research Report RR-83-113. 
15. Mann, W.C. & Matthiessen, C.M.I.M., 1983. 
Nigeh A Systemic Grammar for Text Gen- 
eration, USC/Information Sciences Institute 
Research Report RR-83-I05. 
16. Mann, W.C. & Thompson, S.A., 1983. 
Relational Propositions in Discourse, USC/- 
Information Sciences Institute Research Re- 
port RR-83-115. 
17. Mann, W.C. & Thompson, S.A., 1986. 
Rhetorical Structure Theory: Description 
and Construction of Text Structures, in Nat- 
ural Language Generation: Nero Results in 
Artificial Intelligence, Psychology, and L~n- 
guistics, Kempen, G. (ed), (Kluwer Academic 
Publishers, Dordrecht, Boston MA) 279-300. 
18. Mann, W.C. & Thompson, S.A., 1987. 
Rhetorical Structure Theory: A Theory of 
Text Organization, USC/Information Sci- 
ences Institute Research Report RR-87-190. 
19. Matthiessen, C.M.I.M., 1984. 
Systemic Grammar in Computation: the 
Nigel Case, USC/Information Sciences Insti- 
tute Research Report RR-84-121. 
20. McKeown, K.R., 1982. 
Generating Natural Language Text in Re- 
sponse to Questions about Database Queries, 
Ph.D. dissertation, University Of Pennsylva- 
nia. 
21. Moore, J.D., 1988. 
Enhanced Explanations in Expert and 
Advice-Giving Systems, USC/Information 
Sciences Institute Research Report (forth- 
coming). 
22. Sacerdoti, E., 1977. 
A Structure for Plans and B¢l~avior (North- 
Holland, Amsterdam). 
23. Schank, R.C., 1975. 
Conceptual Information Processing, (North- 
Holland, Amsterdam). 
24. Schmolze, J.G. & Lipkis, T.A., 1983. 
Classification in the KL-ONE Knowledge 
Representation System, Proceeding8 of the IJ- 
CAI Conference, Karisruhe (330-332). 
25. Shepherd, H.R., 1926. 
The Fine Art of Writing, (The Macmillan Co, 
New York). 
26. Shortliffe, E.H., 1976. 
Computer-Based Medical Consultations: 
MYCIN. 
169 
