METAPHORIC GENERALIZATION THROUGH 
SORT COERCION 
Ellen Hays 
10 Pine Avenue 
Arlington, MA 02174 
hays@linc.cis.upenn.edu 
Samuel Bayer 
The MITRE Corporation, A040 
Burlington Rd. 
Bedford, MA 01730 
sam@mitre.org 
Abstract 
This paper presents a method for interpret- 
ing metaphoric language in the context of a 
portable natural language interface. The method 
licenses metaphoric uses via coercions between 
incompatible ontological sorts. The machinery 
allows both previously-known and unexpected 
metaphoric uses to be correctly interpreted and 
evaluated with respect to the backend expert sys- 
tem. 
1 Introduction 
One of the central issues in AI systems has been 
how to model the domain: what are the primitives 
of the ontological language, how are the ontolog- 
ical sorts organized, and so on. AI researchers 
have explored a wide range of object-centered 
and relation-centered representations (for exam- 
ple, Brachman and Schmolze (1985) and Minsky 
(1975)). When setting up the domain model for 
a natural language interface, though, one must 
also keep the lexicon in mind, so that words can 
be defined and processed efficiently; if possible, 
the hierarchical organization of the domain model 
should minimize sense ambiguity, by allowing lex- 
ical items to point to classes that dominate the 
objects that reflect each item's range of meanings. 
However, a growing body of literature argues 
that the generalizations about the world im- 
plied by the lexicon do not correspond exactly 
to standard computational notions of fine-grained 
ontological structure. Rather, the mapping is 
mediated by pervasive low-level metaphoric and 
metonymic processes (as pointed out by Lakoff 
(1987) and others) that make for a mismatch be- 
tween the desired world model and the lexicon. 
At the MITRE Corporation, we are developing 
an interface architecture to support King Kong, 
our portable natural language interface for ex- 
pert systems, and AIMI, our multimedia interface 
for the same class of systems) Portable inter- 
faces provide an additional set of problems be- 
yond simple domain modeling. In particular, in 
our case, the structure the knowledge represen- 
tation imposes on the backend domain model is 
hierarchical and relation-based, and its form must 
be consistent across system ports; thus the knowl- 
edge representation may structure domain-specific 
information in a way that is fundamentally differ- 
ent from the way it is organized in the backend. In 
this context, one needs to develop a computational 
account of the low-level metaphor that creates the 
mismatch between the domain model and the lex- 
icon. In this paper, we will discuss a mechanism 
implemented in King Kong that we call "sort co- 
ercion" that is intended to address that mismatch. 
2 Refinement in the King 
Kong domain model 
In the King Kong knowledge representation, both 
concepts and relations are organized hierarchi- 
cally. King Kong exploits this hierarchy in a num- 
ber of ways, of which the most relevant to this 
discussion occurs in the process of refinement. 
When King Kong interprets a sentence, it builds 
an interpretation corresponding to the input. In- 
terpretations represent a point in the semantic 
1 The AIMI system is, in fact, one of the domains to which 
King Kong has been ported. The current implementation 
of King Kong has also been ported to two mission planning 
systems and one transportation planning system. The co- 
ercion mechanism described here currently supports exam- 
ples in the mission planning and interface domains. 
222 
analysis that is subsequent to some lexical disam- 
biguation but prior to the determination of scope 
relationships and reference resolution. They are 
built in large part out of knowledge representa- 
tion objects. They have heads, for instance, which 
are typically filled by relations from the domain 
model, and argument lists, which are usually map- 
pings from the arguments of the relation in the 
head to other interpretations. 
The heads of these interpretations can be very 
general relations, and King Kong uses refinement 
to find relations in the hierarchy that are dom- 
inated by the head indicated by the input and 
that are specific enough to be evaluated. Once 
referents have been resolved, refinement chooses 
appropriate leaf relations by recursively checking 
the children of each relation in the subgraph acces- 
sible from the input relation and eliminating any 
children whose argument restrictions are disjoint 
from the sorts of the arguments. Each leaf relation 
has backend access code stored on it that allows 
King Kong to communicate with the backend ex- 
pert system. The code stored on the leaf relations 
found by this procedure supports the evaluation 
of the logical expressions generated from the in- 
put interpretations. 
3 Motivations for sort coer- 
cion 
The obvious problem for a system using a hier- 
archy of the kind just described is that in most 
cases there is no direct, one-to-one mapping be- 
tween words and concepts. Most lexical items have 
a number of different meanings, and within those 
meanings there are often different senses, as well 
as various selectional restrictions and preferences, 
whether rigidly defined or merely stylistic. 
One case in point is the locative prepositions, 
which have been studied in great detail by a 
number of linguists, including Herskovits (1986), 
whose analysis of static locative prepositions such 
as in, on, and at defines a program of sorts for in- 
terpreting each, in the presence of particular argu- 
ments. The scheme consists of an ideal meaning (a 
very abstract definition) and a number of use types 
(more concrete senses). The relations so defined, 
however, require that the system have recourse to 
a number of "functions" that, in some sense, "co- 
erce" the objects arguments to the relations from 
one ontological sort to another. 
Herskovits calls these geometric description 
functions; they capture a number of different kinds 
of conceptualization (or recasting) of objects. For 
example, for the purposes of the abstract rela- 
tion at(x,y) ("X \[is\] at y,,),2 both x and y are 
taken to be points. 3 Then in the actual instance of 
the relation at (j olin, airport), according to this 
model, we have conceptualized both of the (three- 
dimensional) objects in the relation as points in or- 
der to express that particular locative relation be- 
tween them. In the same way, when we use at with 
a temporal argument ("a meeting at 5 o'clock"), 
we are in some sense "viewing" a time point as a 
spatial object, namely a geometric point. 4 
Since a geometric description function can ap- 
ply to any argument of the appropriate ontolog- 
ical sort (i.e., within the range of the function), 
regardless of the relation it figures in, what this 
scheme captures is a generalization about concep- 
tual "transfer of reference', as Herskovits has more 
recently called it (Herskovits, 1989). 
The coercion mechanism described in this pa- 
per was inspired partly by Herskovits' work and 
partly by the system's existing domain model. It 
is a response to the need for a one-to-many map- 
ping from lexical items to ontological items (in this 
case locative and event relations), and is an at- 
tempt to capture explicitly some of the ways in 
which changing the way an object is viewed allows 
certain metaphoric and metonymic uses. 
4 The coercion mechanism 
The central information source in our account of 
metaphor and metonymy is a set of coercion rules. 
Coercion rules declare different ways of viewing 
particular classes of objects. So if we wish to view 
temporal intervals as one-dimensional spatial ob- 
jects (lines), we would declare: 
(I) (defCoerce temporal-interval line) 
These coercion rules can be chained; if we wish 
to view events as temporal intervals (that is, the 
intervals over which they occur), we could ulti- 
mately view them as lines as well simply by adding 
another declaration: 
2Herskovlts follows Talmy (1983) and others in seeing 
locative prepositions as defining a figure/ground relation- 
ship between a located object and a reference object. 
3The ideal meaning of at is for two points to coincide 
(1986, p.128). 
4 Jackendoffproposes a similar response to the problem, 
with respect to temporal use of spatial expressions. See 
(Jackendoff, 1983, ch.10). 
223 
(2) (defCoerce 
durative- event 
t emporal-int erval) 
King Kong uses these coercion rules in two re- 
lated ways. The first is to license what we call 
shadow relations. These are relations that have 
no parent but are connected to the domain model 
by means of a shadow link. This link requires 
that the value restrictions on the arguments of the 
shadowing relation be connected to the value re- 
strictions on the shadowed relation by a chain of 
coercion rules. These shadow links are required 
because the normal subsumption relationship does 
not permit the shadowed relations to be connected 
to their shadows; the endpoints of coercion links 
will typically be disjoint. Intuitively, these shadow 
relations represent the metaphoric uses that Lakoff 
called attention to. When King Kong encounters 
a relation pointed to by the input that has shad- 
ows associated with it, it exploits an expanded 
version of the refinement mechanism described in 
Section 2 to search through not only children but 
also shadows for acceptable leaf relations. 
Let us take a brief example. Imagine that we 
wish to capture the low-level metaphor in a sen- 
tence like "The length of the meeting is 5 hours." 
The ideal meaning of the length-of relation in- 
volves a line and a one-dimensional (spatial) mea- 
sure, which are the value restrictions on the two 
arguments (indicated here as vr): 
(3) (defRelation length-of 
(arg object (vr line)) 
(arg measure (vr ld-measure)) 
(super measure-of) ) 
The coercions described in (1) and (2), together 
with a view of quantities of time as spatial mea- 
sures (shown in (4)), suffice to license the shadow 
embodying the temporal metaphoric use of the 
length-of relation in (3): 
(4) (defCoerce 
quant ity-of-tiJne ld-measure) 
(5) (defRelation length-of-event 
(axg event 
(vr durative-event) ) 
(arg measure 
(vr quantity-of-time) ) 
(shadows length-of)) 
But the mechanisms introduced so far do not 
address a particular requirement of the King Kong 
metaphor mechanism that might not be imposed 
on other such mechanisms: the resulting logical 
expressions must be evaluable. Since King Kong is 
an interface, its domain model captures the shape 
of the data, but it does not itself store any facts; 
it must consult an external (i.e., the backend sys- 
tem's) database to reply to any queries. So when 
it recognizes a metaphoric use, it must provide 
the proper backend argument fillers to the back- 
end database in order to evaluate the query. But 
if the metaphoric use of the relation correspond- 
ing to the input has an argument corresponding 
to event and the ideal meaning requires an argu- 
ment corresponding to line, as in the length-of 
relation given above, how can King Kong provide 
the proper backend individuals? 
The answer lies in the way coercion rules inter- 
act with the domain model. When they license 
a shadow relation, they instantiate a point in the 
space of possible coercions, and to this shadow re- 
lation we can attach backend access code that ex- 
pects objects corresponding to the classes in the 
value restrictions of the current (shadowing) rela- 
tions. In other words, in the example given above, 
although conceptually we are viewing an instance 
of event as an instance of line, we need not refer 
to the ideal class at all in processing; the shadow 
relation permits us to treat these instances as or- 
dinary members of the event class. The existence 
of this shadow implies that there is a conceptual 
mismatch between the way the backend system 
records this information and the way language ex- 
presses it; the backend system considers the in- 
put classes directly, while the ontology and lexicon 
view these classes as coercions from other classes. 5 
But what if the backend system requires that 
the input classes be coerced, just as the domain 
model and lexicon do? This is the second way in 
which the coercion rules can support metaphoric 
language. Coercion rules can have fragments of 
logical expressions attached to them that describe 
how to convert items of one class to items of an- 
other. We can use these augmented coercion rules 
to process novel uses of relations. If a path of co- 
ercions can be followed dynamically (rather than 
built at load time, as when shadows are licensed), 
the novel use can be evaluated, as long as the log- 
5This shadow, along with many others, could be auto- 
matically generated from our set of coercion rules, but since 
the backend access code that shadows are "repositories" for 
cannot be automatically generated as well, that would not 
be productive. Furthermore, we acknowledge the possi- 
bility that the unconstrained application of these coercion 
rules would generate shadow relations with no linguistic 
validity. 
224 
ical expressions attached to the coercion rules can 
themselves be evaluated. In that case, the proce- 
dure that builds logical expressions will fold the 
logical expressions associated with the coercion 
rules into the overall logical expression, in order 
to create an evaluable expression, e 
For example, consider a backend system that 
knows about meetings and their start and end 
times, but doesn't store their duration. Further- 
more, it knows how to manipulate intervals of 
time. We might amend the coercion rule in (2) 
above in the following way, and replace the shadow 
shown in (5): 
(e) (defCoerce 
durative-event temporal-interval 
(lambda x 
(durative-event-has-interval 
durative-event x))) 
(7) (defgelation 
durative-event-has-iuterval 
(arg event 
(vr durative-event)) 
(arg interval 
(vr temporal-interval)) 
(super event-has-property)) 
(s) (defRela$ion length-of-interval 
(arg interval 
(vr temporal-interval)) 
(arg measure 
(vr quantity-of-time)) 
(shadows length-of)) 
In this situation, the length-of-interval re- 
lation instantiates a point in the space of possible 
coercions that represents the system's ability to 
compare a temporal interval with a time measure- 
ment. It represents the direct understanding of 
something like "The length of the coffee break was 
10 minutes," where we assume that a coffee break 
is a kind of temporal interval. Ignoring tense, the 
logical expression corresponding to this example 
is: 7 
(9) (length-of coffee-break1 lO-minutes) 
The generalized refinement process will locate 
the shadow length-of-interval and use the 
6If the coercion rules are not all evaluable, we can build 
an interpretation for the input, but we cannot evaluate it. 
? King Kong actually represents measurements as undif- 
ferentiated pools of individuals, much as it represents "10 
planes", for instance. We may ignore that detail here. 
code associated with it to communicate with the 
backend system. We can do more, however. Given 
the existence of the augmented coercion rule, we 
can understand sentences like our first example 
"The length of the meeting is 5 hours" by build- 
ing a chain of coercions that consists of a single 
link, from events to temporal intervals. In this 
case, our logical expression will be: 
(10) (exists y 
(lambda x 
(durat ive-event-has-int erval 
coffee-break1 x) ) 
(length-of y lO-minutes) ) 
As long as there is backend access code asso- 
ciated with the durative-event-has-interval 
relation, we can process this use of the 
length-of relation without the shadow in (5) 
(length-of-event) present. In fact, we can pro- 
cess any metaphoric reference to an event that 
appears in an argument position whose filler is re- 
stricted to intervals of time. Consider the overlap 
relation, whose ideal meaning is a relation between 
two planes or two lines. The coercion rules already 
given will license a shadow that relates two inter- 
vals: 
(xx) (defRelat ion overlap 
(arg obj 1 (vr line)) 
(arg obj2 (vr line)) 
(super static-locative) ) 
(12) (defRelation temporal-overlap 
(arg objl 
(vr temporal-interval)) 
(arg obj2 
(vr temporal-interval)) 
(shadows overlap) ) 
The shadow in (12) corresponds to an example 
like "The current calendar year overlaps with the 
next fiscal year." But given the augmented coer- 
cion rule, we can understand sentences like "The 
first meeting overlaps with the second meeting" 
just as easily: 
(13) (exists y 
(lambda x 
(durat ive-event-has- interval 
meeting1 x) ) 
(exists z 
(lambda x 
(durat ive-event-has-int erval 
meeting2 x)) 
(overlap y z))) 
225 
This method of supporting metaphorical ex- 
tension by explicitly defining the space of pos- 
sible ways of conceptualizing an object allows 
us considerable flexibility in understanding novel 
metaphoric use. s 
The same augmented coercion rules can be used 
if we wish to license a shadow relation that has 
no backend access code associated with it. We 
might want to use that strategy in the situation 
where the metaphoric use can be anticipated but 
the access code associated with the shadow would 
have to perform exactly the same computation as 
the coercion code. 
5 Comparison with other ac- 
counts 
As in DeJong and Waltz's work (1983), the King 
Kong coercion mechanism is triggered by viola- 
tions of sort restrictions on arguments. We do 
not, however, agree with DeJong and Waltz's 
contention that "Nouns are far less likely to be 
metaphorical than verbs." The symbiosis be- 
tween shadows and coercion rules implies that the 
metaphor lies not in the functor or its arguments, 
but rather in the association between them. Fur- 
thermore, our mechanism also structures the path 
between metaphoric use and ideal meaning, and 
provides computational support for argument co- 
ercion. The mechanism has the same advantage 
over the work of Jacobs and Martin. 
5.1 Jacobs and Martin 
In a series of papers (Besemer and Jacobs, 1987; 
Jacobs, 1986; Jacobs, 1987), Paul Jacobs has de- 
veloped a relationship he calls a view. Views 
express a relationship between event types that 
implements metaphoric extension. For example, 
in order to handle examples like "The command 
takes three arguments ~, he defines the following 
view: 
(VIEW execute-operation 
causal-doubl e-trans~ er 
(ROLE-PIAY input object-l) 
(ROLE-PLAY output object-2) 
(ROLE-PlAY user source-l) 
(ROLE-PlAY operation source-2)) 
SNote that shadows always embody dlsjointness between 
at least one of their arguments and those in the ideal mean- 
ing. Thus, no input relation can be simultaneously inter- 
preted both as a subsumed relation and as a shadow. 
In Jacobs' system, this view would incorporate 
the metaphorical mappings from the full range of 
expressions referring to exchange operations such 
as giving, buying, and selling. As a result, the 
mappings in this view may be used to understand 
expressions such as "This command gives you the 
file names", and so on. 
Like the work of Martin (see below), Jacobs' 
approach has the potential for grouping families 
of relationships into situations, a capability King 
Kong does not yet have. Jacobs' views correspond 
roughly to our shadow relations. 
However, the view mechanism provides no lim- 
itations on the correspondences between the ob- 
jects in the ROLE-PLAY declarations, nor does there 
seem to be any capability for computing one argu- 
ment class from another. As a result, it is difficult 
to see how Jacobs' account would intelligently re- 
strict the range of novel language use the system 
will handle, or how it might be used to provide 
computational support for sort coercion in an in- 
terface. 
Martin (1987a, 1987b), working with the same 
mechanism, takes steps toward addressing the 
first concern. His work involves learning new 
metaphoric uses in light of already recognized 
metaphors. So Martin's heuristics allow the sys- 
tem to learn what "getting out of Lisp" means if 
it knows what "getting into Lisp" means. His sys- 
tem knows about entering and exiting, enabling 
and disabling Lisp processes, and that there is 
a map between entering and enabling Lisp. Be- 
cause entering and exiting are closely connected 
(they are related by the frame semantic relation 
reversible-state-change), Martin's system can 
build the metaphoric link from exiting to disabling 
Lisp. Techniques such as this one constrain the in- 
terpretation of novel language use, since the sys- 
tem can only generalize from the existing library of 
metaphoric uses. However, they provide no com- 
putational support for evaluating novel uses. 
5.2 Gentner et al. 
Gentner's structure-mapping techniques (Gen- 
tner, 1983; Gentner et al. 1987) are applicable 
mostly to explicit analogies such as "An electric 
battery is like a reservoir." Her approach, imple- 
mented by Falkenhainer and Forbus (1986), maps 
the structure of the source of the metaphor to the 
structure of the target by creating match hypothe- 
ses between relational representations of the base 
and target using a set of match construction rules. 
But the central example of a match construction 
226 
rule seems to require that the names of the predi- 
cates in the facts being matched be identical. Un- 
der this sort of construction rule, it is possible to 
derive a metaphoric mapping only if the names 
of the predicates have been set up to encode the 
metaphor ahead of time. Under this system, it is 
not possible to deduce new metaphors; in fact, one 
can only recognize them if the metaphoric link has 
been made but not recorded. 
5.3 Boguraev and Pustejovsky 
Boguraev and Pustejovsky (1990) argue that the 
normal conceptions of the structure of the lexicon 
are impoverished for two major reasons. First, a 
great number of distinctions beyond those usually 
made are necessary to capture the essential as- 
pects of lexical semantics. Second, the common 
technique for representing ambiguity in the lexi- 
con (enumeration) falls short because enumeration 
of word senses neither organizes the senses intelli- 
gently nor provides for creative use of words. 
For instance, under the enumeration method, 
the following uses of "fast" require that at least 
these three senses he listed in the lexicon: 
:fast(l): able to move quickly (a fast 
car) 
fast(2): able to perform some act 
quickly (a fast typist) 
faat(3): taking little time (a fast oil 
change) 
However, these three senses are not enough to ac- 
count for the creative use of "fast" in a phrase such 
as "a fast highway". 
Pustejovsky's solution to this problem (outlined 
also in (Pustejovsky, 1990)) is a "generative lex- 
icon", which organizes lexical items with respect 
to one or more of: (1) argument structure, (2) 
event structure, (3) qualia structure, and (4) lexi- 
cal inheritance structure. These lexical structures 
are intended to address the different ways in which 
words are understood; the differing interpretations 
of "fast" shown above are taken to be a function of 
the differing qualia structures of "car", "typist", 
"oil change", and "highway". 
While Pustejovsky's proposal for a variety of 
lexical structures is far richer than anything cur- 
rently implemented in King Kong, one problem 
with his account is that the links are links be- 
tween lexical items and not between objects in a 
domain model. Simple cases of anaphoric refer- 
ence demonstrate that in many cases the coercions 
that he conceives of are properties not of lexical 
items but rather of the objects referred to: 
John bought a Porsche, and it's fast. 
John hired a typist, and he's fast. 
I drove down 1-90 yesterday, and it's 
fast. 
John bought a new car, but Bill's is 
faster. 
John hired a good typist, but Bill's is 
faster. 
America is supposed to have good high- 
ways, but Italy's are faster. 
The lexical items whose qualia structures are in- 
tended to account for the different interpretations 
of "fast" are not present in the second clause of 
each of the preceding examples, but the correct in- 
terpretations are still available. This implies that 
it is the language user's conception of the object 
in question (that is, the user's world model) that 
determines the precise sense of "fast". 
In our account, in contrast, the links that sup- 
port the range of metaphoric extensions Puste- 
jovsky deals with reside in the domain model. This 
account also supports generalization of these ex- 
tensions to hierarchies of semantic classes: 
John bought a new car, and it's fast. 
John bought a new vehicle, and it's fast. 
and preserves these extensions under synonymy: 
John bought a new car, and it's fast. 
John bought a new automobile, and it's 
fast. 
6 Conclusion 
One insight missed in most relation-based ac- 
counts of metaphor 9 is the wide space of possibil- 
ities for conceptualizing the argument types: how 
these possibilities are constrained, how the trans- 
formations can be computed. The coercion mecha- 
nism in King Kong supports metaphoric processes 
both statically and dynamically, by defining how 
metaphoric links between relations are established 
and supporting computational tools for compre- 
hending and processing novel metaphoric uses. 
Acknowledgments 
This research was supported by the MITRE Cor- 
poration under MSR project 91340. 
9 With the exception of Boguraev and Pustejovsky's, of 
COUlee. 
227 

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