RIDDLES: ACCESSIBI1JTY AND KNOWRID(H'I I{I':t)I¢I~SI'INTA'I'ION 
PAUL I)E PALMA 
Gonzaga University, Spoklnle, U~A 
E. JUD1Ttt WEINI'IR 1 
Temple University, Philadelphia, USA 
1. INTRODUCTION 
In another study (Weiner and De Pahna 
199,31, we determined that accessibility 
hierarchies among the meanings of po\]ysemous 
words play an ilnportant role in the generation 
and comprehension of a category of sinlple 
riddles, In that paper and this one we restrict 
our attention to riddles that fit this definition: 
d :/d(//e cons/Ms ol:a se)~/e se/e/enee <lues/ean 
\[/FQJ /o/lowed by a s///g/: se/s/e/see aesswe/" ill/A/. 
//)e //Q flrese/e/s ~7 col/eel/by/o/a///':3u/e,s' 
wh/bl/ c'a:) apply to///ore/hal/o:/e AlP, /hu, r 
a//o#:/?/S :no/'e /he// one an.type/"/o/lye </uesl/m:. 
a/eddie an, re:el" \[#d) a/:d a .qm:~kl e/ns/v:.,:: 
/~e R~ //a,r bee:/eo/e.s'D"ueled :~ suc/: o way u,r 
1o lead/lie/'/rid/re/opre~'z"/he .5't/reght 
answe:" over ttJe tIA. 
Lexical ambiguity is at the center of riddles of 
this type. Consider this riddle: 
(1) RQ: What has a mouth and eamlot eat? 
RAp A river. 
Here the riddler intends by Iil~h the inanimate 
meaning but the sentence is constructed so that 
the anilnate one is the more natural reading, tn 
our 1993 paper, we showed how the existence of 
aeeessibililty hierarchies could account for this 
preference. 
We now turn our attention to the need to 
build this mechanism into any knowledge 
representation language that hopes to capture 
the full subtlety of natural language since it is 
our contention that riddles, violate the rules of 
normal discourse and thus represent a useful 
way % approach the study of these rules. To 
that end, we present a knowledge representation 
plan along wit.h an algorithm that uses the 
repl'esentatiou in order to generate riddles. 
Althou~g~ the representational structures that we 
use are in the style of lKl, ONE (Braehman and 
Sehlnolze), this is purely a convenience. 
Accessibility hierarci)ies must be built into ~ly 
system which can process natural language as 
well as one which can play the riddling game. 
2. ACC\]U',SSIBIIJTY IIII'~RAR(Jlt\[I:,S 
Cognitive psychologists have long recognized 
that people form taxonomic eaLegories (l{oseb 
1978) wilt) smnc members heiul; more typical 
instances of those categories than others. This 
graded structure is not limited to taxonomic 
categories btR seems to include such .tmlikely 
possibilities as formal categories like the 
category of odd numbers a\]ld that of sq!Jares 
(Armstrong, Gleitman, and GMtman 19831 and 
linguistic categories for phones, phonemes and 
syntactic structures (Lakoff 1986). In recent 
years, researchers have shown that categories 
arc not structurally invarianL but are, in fact, 
demonstrably unstable (l/arsalou 1987). Their 
graded structure varies with >leh factors as 
linguistic context and point of view and even 
differs for the same individual over time. 
The formation of ad hoe categories to 
accomplish specific /*oals (Harsalou 19fl3) is 
another area of instability irl human category 
formation, l"or example, the category 
"things to-take-out-of-a burning /louse" 
might include subordinate categories like 
1The order of the names of the two authors is arbitrary. This paper represents tile joint 
work of both authors. 
AClEs DE COLING-92, NAIgllKS, 23-28 AOUI' 1992 1 1 2 1 Pl~oc. OF COLING-92, NANTES, AUO. 23-28, 1992 
"children," "jewels," "paintings." and "portable 
tv's" (Murphy and Medin 1985) and is formed 
only when one's house is burning or during a 
discussion like this one. Ad hoe categories, once 
formed, function similarly to more traditional 
categories, As we show later in this paper, ad 
hoe category formation is an important 
component in the generation or solution of 
riddles, 
A model that is to account for the 
human tendency to form categories must 
account for both the stable and the unstable 
aspects. Barsalou's approach to the instability 
in categories is to recognize the existence of 
both context-independent and 
context-dependent information m long-term 
memory, where it is arranged as interrelated, 
continuous knowledge. It is the 
context-independent information that is most 
likely to be shared by a large number of 
individuals within a speeeh community. Its 
activation is obligatory, When one thinks of 
r_cbilla, for example, "red-breasted" springs to 
mind whereas "poisonous" is triggered by 
rattlesnakes. Context dependent information, by 
contrast, is aeeessed only within a relevant 
context So "hard" may be triggered by ice while 
diseussing/all. The instability of categories is 
accounted for by different information for a 
given category being compiled in working 
memory at different times depending on the 
situation. Some information, e.g., eontext- 
independent information, is more aceessible than 
other information, 
We have extended this model (Weiner and 
De Palma 1993) to explain the tendency of 
people to think of the mouth of a person before 
mouth of a river in (1) above, Given the 
presumed universality of certain principles 
governing eategorization, it seems likely that, in 
context neutral situations sueh as (1), 
ambiguous words form ad hoe category-like 
structures of their multiple meanings onto which 
an aecessibility hierarchy is imposed. For 
example, in (1), there is a category-like 
structure corresponding to the phonemic 
realization of the word mo_ulk to whieh the 
different meanings belong; ill (1), one thinks of 
the mouth of a person before tile mouth of a 
river. 
3. THE KNOW~DGE BASE 
We thus offer our exposition of the structure 
that underlies the kind of lexieal ambiguity 
found in riddles as linguistic evidence for the 
episteinologieal requirements of a knowledge 
representation system whieh can support both 
normal discourse and riddles. Riddles will use 
the knowledge in one way: normal discourse will 
use it ill another. The representation will 
remain the same; only the algorithms will differ. 
Consider Figure 1, a knowledge-base 
fragment in the style of KL-ONE that contains 
the information necessary to generate or solve 
riddle (1). The KL-ONE entities most relevant to 
this discussion are Concepts (diagrammatieally 
represented by ellipses) and RoleSets 
(represented by encircled squares). The Concept 
is the primary representational entity. For us, 
it represents the category of objects indicated by 
the Concept name, Thus, in Figure 1, Concepts 
stand for the category RIVER-MOUTH, the 
category ANIMATE_MOUTlt, and so on. Concepts 
are connected to one another by superC links, 
represented in tile figures by double arrows, A 
superC link indicates that tile subordinate 
Concept (subConeept) stands in an inheritance 
and subsumption relationship with the 
superordinate Concept (superConeept). (The 
higher Concept subsumes the lower ojae; the 
lower one inherits from the higher one). Thus, 
PERSON_MOUTH is an ANIMATLMOUTH and a 
MOUTH, 
In our knowledge base, RoleSets represent, 
predicates of a Concept, the fi!\]ers of which, 
known as Value Restrictions (v/r's}, are 
themselves Concepts. So PERSON-MOUTH has a 
RoleSet "funetion" with the filler EAT, meaning 
ill our representation that a function of a 
person's mouth is to eat, {Of course there are 
others not shown here). 
f'urther, each RoleSet filler has a number 
restriction represented by two numbers within 
parentheses. These represent the lower and 
upper bounds on the number of fillers for a 
AcrEs DE COTING-92, NA~, 23-28 AO~'T 1992 I 1 2 2 Pnoc. OF COL1NG-92, NANTES, AUG. 23-28, 1992 
MOUTH 
ANIMATEMOUTH 
INANIMATE_\] 
function 
(5,5) 
v/r 
EAT 
PERSON MOUTH 
body~parts 
PERSON 
Figure 1 
RIVER MOUTH 
~ function v/r, (2,2) 
/~ parts 
EXITPOINT 
KL-ONE-Iike representation of a portion 
of the knowledge needed to generate or solve: 
What has a mouth and does not speak? 
ACRES DE COL1NG-92, NANTES, 23-28 AOUT 1992 1 1 2 3 PRO(:, OF COLING-92, NANTI~S, AUG. 23-28, 1992 
given RoleSet, In Figure 1, we have arbitrarily 
estimated that people's mouths have a minimum 
of 6 and a maximum of 5 functions, 
Notiee that every Concept has a 
diamond-shaped symbol associated with it. This 
symbol is not part of the KL-ONE language. We 
are introdueing it here as a new primitive, 
l.edfical, which contains lexieal information about 
a Concept, For our purposes, Lexieal contains 
the phonemic representation of a Concept 
(although, for simplieity in this figure, only 
certain phonemic representations are aetually 
provided). This arrangement allows us to 
acknowledge the relationship between a Concept 
and the word used to name the Concept without 
asserting that they are the same thing, 
separating meanings of polysemous words from 
their phonemic representation, 
As discussed above, ambiguous 
(polysemous, homophonous) words can form ad 
hoe eategory-like structures of their multiple 
meanings. Thus, we can have a superConeept 
MOUTH, a category of polysemous words, with 
subConeepts ANIMATE_MOUTH and 
INANIMATE_MOUTH. We reeognize the probability 
that in the ease of ambiguous forms with a 
choice of animate vs, inanimate meaning, the 
animate one is thought of before the inanimate 
one (Weiner and De Palma 1993), So the ideas 
eneoded in Figure 1, although not explicitly 
spelled out with respect to aeeessibility, are 
based on the assumption that, in 
context-independent situations, people tend to 
think of animate things before they think of 
inanimate ones. 
In riddle (2), 
(2) RQ: What has four legs and only one foot? 
RA: A bed. 
we model the riddling process by assuming that 
the phrase Lo~ ke4~ causes the formation of an 
ad hoe eategory "four legged thing." A 
representation of a portion of the knowledge 
needed to generate or solve riddle (2) will be 
given in a future paper. 
4. THE ALGORITHM 
The following algorithm refers to Vqgure 1 
and will generate riddle (1). The algorithm 
requires three functions: 
1, I"indI-loms(HC1,HC2,C1,C2) - searches the 
knowledge base for two homophonous Coneepts, 
HC1 and HC2 where HCl and HC2 are the value 
restrictions of two Coneepts' RoleSets. Call these 
Concepts Cl and C2. CI must eontain the more 
accessible (i.e. in these examples, 
eontext-independent, animate) eoneept. For 
example, after an application of 
FindHoms(llCl,HC2,C1,C2), on the KB fragment 
contained in Figure 1, the variables would look 
like this: 
HCI <---PERSON_MOUTH 
CI < --- PERSON 
HC2 < .... RIVER_MOUTH 
C2 <--- RIVER 
Note that HCI contains PERSON_MOUTH, a value 
restriction of C1 (PERSON), tIC2 contains 
RIVER_MOUTH, a value restrietion 
of C2 (RIVER) and the Concept in C1 (PERSON) is 
a more accessible Coneept than tile one in C2 (RIVER). 
2. Lex(A,B) -. returns in B the word by which a 
Concept, A, is known. Remember that the 
phonemic representation of this word is 
contained in "Lexieal" (represented in the figure 
by tile diamond shape) for each eoneept. For 
example, Lex(RIVER_-MOUTH,H) returns/mawO'/in 
13, 
:3, MisMateh(CI,C2,HC1,HC2,Type,RSVR) - examines 
the knowledge base (KB) for a mismateh of the 
following type: 
HCI has a RoleSet value restrietion (RSVR) 
that He2 does have. hi Figure 1, this RSVR for 
HC1 would be EAT. Mismatch returns this in 
RSVR. Thus, using Figure 1, Mismatch would 
return EAT in RSVR. Note that HC1 is more 
aeeessible than HC2 by virtue of being animate. 
The algorithm, then, looks like this: 
Acrlm DE COLING-92, NANTES, 23-28 Aou'r 1992 1 1 2 4 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 
Riddle-GenO 
Fmdlloms (HC1,HC2,C1,C2); 
MisMaLch(C1,C2,HC1,HC2,Type,RSVR); 
Print "What has Lex(HC1) and ~t,ex(RSVR)?"; 
End. 
It should be noted that, in the interest of 
simplicity, we have eonflated tile issues involved 
ill generating or solving riddles. Once you know 
the heuristic with which riddles of tile type 
considered in this paper are constructed and 
have created a KB of Concepts, generation is a 
simple matte,'. Solution, of course, is the 
inverse of this algorithm 
5. CONCLUSIONS 
Our examples in this paper use KL-ONE as a 
convenient model of a knowledge representation 
system. We propose the addition of accessibility 
as all important epistemological primitive to the 
KL-ONE system since it appears critical to build 
this factor into any knowledge base which can 
both support a system for natural language 
processing and be used for certain kinds of 
humor. Our work also highlights other 
requirements for knowledge representation 
systems capable of supporting natural language: 
1. Links between the phonemic representation 
of linguistic entities and their associated 
concepts (Lexical) 
2. The necessity of representing homophonoua 
categories 
3. The ability to form ad hoe categories such as 
those based on homophonous phrases 
REFERENCES 
1. Armstrong, S., L. Gleitman, and H. gleitman 
1983 What some concepts might not be. 
~gnihon 13: 263-308. 
2, Barsalou, L.W. 1983 Ad hoc categories, ~rm:y 
11(3): 211-227. 
3. Barsalou, L.W. 1987 The instability of graded 
structure: implications for the nature of 
concepts. In U. Neisser (ed.), Concepts and 
(:~kLt~ devclopmenLi ~gi~ 
i~l~ckuoliaoA~or,~il~ategorization. New York: 
Cambridge University Press, 101-140. 
4. Brach,nan, R.£ and J.G. Schmolze 1985 An 
overview of tile KL-ONE knowledge 
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171 216. 
5. Lakoff, (;, 1986 ~o3amm Iir~ge~ua 
Lhinga~. W~hikl~cigcgories tell usxd~oxtkLheAl~tur~ 
¢{_Ul~ght. Chicago: University of Chicago Press. 
6. Murphy, G. and D. Medin 1985 The role of 
theories m conceptual coherence, ~icall 
l~e_v3e_w 92(3): 289-316. 
7. Peters, S., S, Silapiro and W. Rapaport 1988 
Flexible natural language processing and 
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~t.h_knnn~LC~'ence of the~gml~e.~8_cie.n¢~ 
;~ctcdegv: 125 131. 
8. Roseh, E.H. 1978 Principles of categorization. 
In E. Roseh and B.B. Lloyd (eds.), Cognition and 
~~. Hillsdale, NJ: Lawrence Erlbaum 
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press). 
ACKNOWleDGMENTS 
We would like to thank David Weiner for his 
invaluable advice and assistance during the 
preparation of the final manuscript, 
ACTES DE COLING-92, NANTES, 23-28 aoCrr 1992 I 1 2 5 t'ROC:. OF COL1NG-92, NAtCrEs, AUG. 23-28, 1992 
