A HYBRID APPROACH TO REPRESENTATION IN THE 
JANUS NATURAL LANGUAGE PROCESSOR 
Ralph M. Weischedel 
BBN Systems and Technologies Corporation 
10 Moulton St. 
CambHdge, MA 02138 
Abstract 
In BBN's natural language understanding and 
generation system (Janus), we have used a hybrid 
approach to representation, employing an intensional 
logic for the representation of the semantics of ut- 
terances and a taxonomic language with formal 
semantics for specification of descriptive constants 
and axioms relating them. Remarkably, 99.9% of 
7,000 vocabulary items in our natural language ap- 
plications could be adequately axiomatlzed in the 
taxonomic language. 
1. Introduction 
Hybrid representation systems have been ex- 
plored before \[9, 24, 31\], but until now only one has 
been used in an extensive natural language process- 
ing system. KL-TWO \[31\], based on a propositional 
logic, was at the core of the mapping from formulae to 
lexical items in the Penman generation system \[28\]. 
In this paper we report some of the design decisions 
made in creating a hybrid of an intensional logic with a 
taxonomic language for use in Janus, BBN's natural 
language system, consisting of the IRUS-II under- 
standing components \[5\] and the Spokesman genera- 
tion components. To our knowledge, this is the first 
hybrid approach using an intensional logic, and the 
first time a hybrid representation system has been 
used for understanding. 
In Janus, the meaning of an utterance is 
represented as an expression in WML (World Model 
Language)\[15\], which is an intensional logic. 
However, a logic merely prescribes the framework of 
semantics and of ontology. The descriptive 
constants, that is the individual constants (functions 
with no arguments), the other function symbols, and 
the predicate symbols, are abstractions without any 
detailed commitment to ontology. (We will abbreviate 
descriptive constants throughout the remainder of this 
paper as constants.) 
Axioms stating the relationships between the con- 
stants are defined in NIKL \[8, 22\]. We wished to ex- 
plore whether a language with limited expressive 
power but fast reasoning procedures is adequate for 
core problems in natural language processing. The 
NIKL axioms constrain the set of possible models for 
the logic in a given domain. 
Though we have found clear examples that argue 
for more expressive power than NIKL provides, 99.9% 
of the examples in our expert system and data bass 
applications have fit well within the constraints of 
NIKL. Based on our experience and that of others, 
the axioms and limited inference algorithms can be 
used for classes of anaphora resolution, interpretation 
of highly polysemous or vague words such as have 
and with, finding omitted relations in novel nomina/ 
compounds, and selecting modifier attachment based 
on selection restrictions. 
Sections 2 and 3 describe the rationale for our 
choices in creating this hybrid. Section 4 illustrates 
how the hybrid is used in Janus. Section 5 briefly 
summarizes some experience with domain- 
independent abstractions for organizing constants of 
the domain. Section 6 identifies related hybrids, and 
Section 7 summarizes our conclusions. 
2. _Commitments to Component Hepresentation Formalisms 
We chose well-documented representation /an- 
guages in order to focus on formally specifying 
domains and using ~hat specification in language 
processing rather than on defining new domain- 
independent representation languages. 
A critical decision was our selection of intensional 
logic as the semantic representation language. (Our 
motivations for that choice are covered in Section 
2.1.) Given an intensional logic, the fundamental 
question was how to support inference for semantic 
and discourse processing. The novel aspect of the 
design was selecting a taxonomic language and as- 
sociated inference techniques for that purpose. 
2.1. Why an Intensional Logic 
First and foremost, though we had found first- 
order representations adequate (and desirable) for NL 
interfaces to relational data bases, we felt a richer 
semantic representation was important for future ap- 
plications. The following classes of representation 
challenges motivated our choice. 
• Explicit representations of time and world. 
Object-oriented simulation systems were an ap- 
plication that involved these, as were expert 
systems supporting hypothetical worlds. The 
underlying application systems involved a tree 
of possible worlds. Typical questions about 
these included What if the stop time were 20 
hours? to set up a possible world and run a 
193 
simulation, and In which situations is blue attri. 
tion greater than 50%? where the whole tree of 
worlds is to be examined. The potential of time- 
varying entities existed in some of the applica- 
tions as well, whether attribute values (as in 
How often has U$$ Enterprise been C3?) or 
entities (When was CV22 decommissioned~ 
The time and world indices of WML provided 
the opportunity to address such semantic 
phenomena (though a modal temporal logic or 
other logics might serve this prupose). 
• Distributive/collective quantification. Collective 
readings could arise, though they appear rare, 
e.g., Do USS Frederick's capabilities include 
anti.submarine warfare or When did the ships 
collide? See \[25\] for a computational treatment 
of distributive/collective readings in WML. 
• Generics and Mass Terms. Mass terms and 
generally true statements arise in these applica- 
tions, such as in Do nuclear carriers carry JP5?, 
where JP5 is a kind of jet fuel. Term-forming 
operators and operators on predicates are one 
approach and can be accommodated in inten- 
sional logics. 
• Propositional Attitudes. Statements of user 
preference, e.g., I want to leave in the 
afternoon, should be accommodated in inter- 
faces to expert systems, as should statements 
of belief, I believe I must fly with a U.S. carrier. 
Since intensionel logics allow operators on 
predicates and on propositions, such state- 
ments may be conveniently represented. 
Our second motivation for choosing intensional 
logic was our desire to capitalize on other advantages 
we perceived for applying it to natural language 
processing (NLP), such as the potential simplicity and 
compositionality of mapping from syntactic form to 
semantic representation and the many studies in lin- 
guistic semantics that assume some form of inten- 
sional logic. 
However, the disadvantages of intensional logic 
for NLP include: 
• The complexity of logical expressions is great 
even for relatively straightforward utterances 
using Montague grammar\[21\]. However, by 
adopting intensional logic while rejecting Mon- 
tague grammar, we have made some inroads 
toward matching the complexity of the proposi- 
tion to the complexity of the utterance; that 
simplicity is at the expense of using a more 
powerful semantic interpreter and of sacrificing 
compositionality in those cases where language 
itself appears non-compositional. 
• Real-time inference strategies are a challenge 
for so rich a logic. However, our hypothesis is 
that large classes of the linguistic examples re- 
quiring common sense reasoning can be 
194 
handled using limited inference algorithms on a 
taxonomic language. Arguments supporting 
this hypothesis appear in \[2, 13\] for interpreting 
nominal compounds; in \[6, 7, 29\], for common 
sense reasoning about modifier attachment; 
and in \[32\] for phenomena in definite reference 
resolution. 
This second disadvantage, the goal of tractable, 
real.time inference strategies, is the basis for adding 
taxonomic reasoning to WML, giving a hybrid 
representation. 
2.2. Why a Taxonomic Language 
Our hypothesis is that much of the reasoning 
needed in semantic processing can be supported by a 
taxonomy. The ability to pre-compile pre-specified 
inferential chains, to index them via concept name 
and role name, and to employ taxonomic inheritance 
for organizing knowledge were critical in selecting 
taxonomic representation to supplement WML. 
The well-defined semantics of NIKL was the basis 
for choosing it over other taxonomic systems. A fur- 
that benefit in choosing NIKL is the availability of 
KREME \[1\], which can be used as a sophisticated 
browsing, editing, and maintenance environment for 
taxonomies such as those written in NIKL; KREME 
has proven effective in a number of BBN expert sys- 
tem efforts other than NLP and having a taxonomic 
knowledge base. 
In choosing NIKL to axiomatize the constants, one 
could use its built-in, incomplete inference algorithm, 
the classifier \[27\]. In Janus, the classifier is used only 
for consistency checking when modifying or loading 
the taxonomic network; any concepts or roles iden- 
tiffed by the (classifier as identical are candidates for 
further axiomatization. Our semantic procedures do 
not need even as sophisticated an algorithm as the 
NIKL classifier; pre-compiled, pre-defined inference 
chains in the network are simpler, faster, and have 
proven adequate for NLP in our applications. 
2.3. Two Critical Choices in the Hybrid 
2.3.1. Representing Predicates of Arbitrary Arity 
Choosing a taxonomic language, at least in cur- 
rent implementations, means that one is restricted to 
unary and binary predicates. However, this not a 
limitation in expressive power. One can represent a 
predicate P of n arguments via a unary predicate P' 
and n binary predicates, which is what we have done. 
(P rl ..... m) will be true iff the following expression is. 
(3 b) (^ (r \]:)) (R1 b r\].) (R2 b r2) ... (Rn b rn)) 
Davidson \[5\] has argued for such a representation of 
processes on semantic grounds, since many event 
descriptors appear with a variable number of ar- 
guments. 
2.3.2. Time and World Indices 
Any concept name or role name in the network is 
a constant in the logical language. We use concepts 
only to represent sets of entities indexed by time and 
world. Roles are used only to represent sets of pairs 
of entities, i.e., binary relations. Given time and world 
indices potentially on each constant in WML, we must 
first state the role those indices play in the NIKL por- 
tion of the hybrid. 
(1, go) 
Figure 1: Two Typical Facts Stated in NIKL 
In a first-order extensional logic, the normal 
semantics of SUPERC and of roles in NIKL are well 
defined \[26\]. For instance, the diagram in figure 1 
would mean 
(V x)((a x) = (a x)) 
(V x)((a x) = (3yX^(C y) (R x y))). 
Due to a suggestion by David Stallard, we have 
chosen to interpret SUPERC and the role link 
similarly, but interpreted under modal necessity, i.e., 
as propositions true at all times in all worlds. Thus in 
the diagram in Figure 1, (A z), (B z), (C z), and (R x y) 
are intensions, i.e., functions with arguments of time 
and world \[t, w\] to extensions. Rewriting the axioms 
above by quantifying over all times and worlds, the 
axioms for the diagram in Figure 1 in the hybrid 
representation are 
(V x)(V t)(V w)((B x)(t..,\] ~ (A x)\[t.w\]) 
(v x)(V O(V w)((B x)\[t,w\] 
(3 y)(^ (C y)\[t.w\] (R x y)\[t.w\])). 
Though this handles the overwhelming majority of 
constants we need to axiomatize, it does not allow for 
representing constants taking intensional arguments 
because the axioms above allow for quantification 
over extensions only)The semantics of predicates 
which should have intensions as arguments are unfor- 
tunately specified separately. Examples that have 
arisen in our applications involve changes in a reading 
on a scale, e.g., USS Stark's readiness downgraded 
from C1 to C4. 2 We would like to treat that sentence 
as: 
(^ (DOWNGRADE a) 
(SCALE a (\[NTENS\[ON Stark-readiness)) 
(PREVIOUS a C1) 
(NEW a C4)). 
That is, for the example we would like to treat the 
scale as intensional, but have no way to do so in 
NIKL. Therefore, we had to annotate the definition of 
downgrade outside of the formal semantics of NIKL. 
Only 0.1% of the 7,000 (root) word vocabulary in our 
applications could not be handled with NIKL. (The 
additional problematic vocabulary were upgrade, 
project, report, change, and expect.) 
3. Example Representational Decisions 
Here we mention some of the issues we focussed 
on in developing Janus. The specification of WML 
appears in \[15\]; specifications for NIKL appear in 
\[22, 26\]. 
Few constants. One decision was to use as few 
constants as possible, deriving as many entities as 
possible using operators in the intensionai logic. In 
this section we illustrate this point by showing how 
definitely referenced sets, information about kinds, in- 
definitely identified sets, and generic information can 
be stated by derivation from a single constant whose 
extension is the set of all individuals of a particular 
class. 
Some of the expressive power of the hybrid is 
illustrated below as it pertains to minimizing the con- 
stants needed From the constants BLACK-ENTITIES, 
GRAY-ENTITIES, CATS and MICE, the operators 
THE, POWER, KIND, and SAMPLE are used to 
derive the entities corresponding to definite sets, 
generic classes, and indefinite sets. In a semantic 
network without the hybrid, one might choose (or 
need) to represent each of our derived entities by a 
node in the network. Our use of the operator THE, 
and the operator POWER for definite plurals follows 
Scha \[25\]. The operators KIND and SAMPLE follow 
Cad.son's analysis \[10\] of the semantics of bare 
plurals. 
THE, as an operator, takes three arguments: a 
variable, a sort (unary predicate), and a proposition. 
Its denotation is the unique salient object in context 
such that it is in the sort and such that if the variable is 
bound to it, the proposition is true. POWER takes a 
sort as argument and produces the predicate cor- 
responding to the power set of the set denoted by the 
sort. These operators are useful for representing 
definite plurals; the black cats would be represented 
as (THE x (POWER CATS) (BLACK-ENTITIES x)). 
vlt is possible that one could extend NIKL semantics to allow for inter~sional aK3uments . but this has not been done. 2An analogy in more common terminology would be His tempera- 
ture dropped from 104 degrees to 99 degrees. 
195 
SAMPLE takes the same arguments as THE, but 
indicates some set of entities satisfying the sort and 
proposition, not necessarily the largest set. KIND 
takes a sort as argument, and produces an individual 
representing the sort; its only use is for bare plurals 
that are surface subjects of a generic statement. If we 
are predicating something of a bare plural, KIND is 
used; for instance, cats as in cats are ferocious is 
represented as (KIND CATS). An indefinite set aris- 
ing as a bare plural in a VP is represented using 
SAMPLE; for instance, gray mice as in Cats eat gray 
mice is represented as (SAMPLE x MICE (GRAY- 
ENTITIES x)). 
The examples above demonstrate that an inten- 
sional logic enables derivation of many entities from 
fewer constants than would be needed in NIKL or 
other frame-based systems. The next example il- 
lustrates how the intensional logic lets us express 
some propositions that can be stated in many seman- 
tic network systems, but not in NIKL. 
Generic assertions. Generic statements such as 
Cats eat mice are often encoded in a semantic net- 
work or frame system. This is not possible in the 
semantics of NIKL, but is possible in the hybrid. The 
structure in Figure 2 would not give the desired 
generic meaning, but rather would mean (ignoring 
time and world) that 
(V x) ((CATS x) = (3 y)(^ (MICE y)(EAT x y))), 
i.e., every cat eats some mouse. 
EAT 
(1,oo) 
Figure 2: Illustration Distinguishing NIKL Networks 
from other Semantic Nets 
Again, following Carlson's linguistic analysis \[10\], in 
the hybrid we would have a generic statement about 
the kind corresponding to cats, that these eat in- 
definitely specified sets of mice. GENERIC is an 
operator which produces a predicate on kinds, intui- 
tively meaning that the resulting predicate is typically 
true of individuals of the kind that is its argument. Our 
formal representation (ignoring tense for simplicity) is 
(GENERIC (LAMBDA (x) 
(EAT x(SAMPLE y MICE)))) (KIND CATS). 
Next we illustrate a potential powerful feature of 
the hybrid which we have chosen not to exploit. 
Derivable definitions. The hybrid gives a powerful 
means of defining lexical items. To define pi/o~ one 
wants a predicate defining the set of people that typi- 
cally are the actors in a flight, i.e., 
(LAMBDA (x') 
{ ^ (PERSON x') 
(GENERIC (LAMBDA (x) 
(3 y)(^ (FLYING-EVENT y) 
(ACTOR y x)))) x') }) 
Though the hybrid gives us the representational 
capacity to make such definitions, we have chosen as 
part of our design no_._tt to use it. For to use it, would 
mean stepping outside of NIKL to specify constants, 
and therefore, that the reasoning algorithms based on 
taxonomic semantics would not be the simple, ef- 
ficient strategies, but rather might require arbitrarily 
complex theorem proving for expressions in inten- 
sional logic. 3 
4. Use of the Taxonomy in Janus 
By domain mode/we mean the set of axioms en- 
coded in NIKL regarding the constants. The domain 
model serves several purposes in Janus. Of course, 
in defining the constants of our semantic represen- 
tation language, it provides the constants that can ap- 
pear in formulae that lexical items map to. For in- 
stance, vessel and ship map to VESSEL. In the ex- 
ample above regarding pilot, the constants were PER- 
SON, FLYING-EVENT, and ACTOR; in the formula 
• above stating that cats eat mice, the constants were 
EAT, MICE, and CATS, 
In this section, we divide the discussion in three 
parts: current uses of the domain model in Janus; a 
plausible, but rejected use; and proposals for its use, 
but not yet implemented. 
4.1. Current Uses 
4.1.1. Selection Restrictions 
The domain model provides the semantic classes 
(or sorts of a sorted logic) that form the primitives for 
selection restrictions. Its use for this purpose is nei- 
ther novel nor surprising, merely illustrative. In the 
case of deploy, a MILITARY-UNIT can be the logical 
subject, and the object of a phrase marked by to must 
be a LOCATION. Almost all selection restrictions are 
based on the semantic class of the entities described 
by a noun phrase. That is, almost all may be checked 
by using taxonomic knowledge regarding constants. 
A table of semantic classes for the operators dis- 
cussed earlier is provided in Figure 3. Though the 
logical form for ~e carriers, all carriers, some carriers, 
a carrier, and carriers (both in the KIND and SAMPLE 
case) varies, the selection restriction must check the 
=USC/ISI \[19\] has proposed e first-order formula defining the set of 
items that have ever been the actor in a flight. Their definition is 
solely within NIKL using the QUA link \[14\], which is exactly the set of 
fillers of a slot. While having eve._..rr flown could be a sense of pilot, it 
seems less useful than the sense of normally flying a plane. 
196 
NIKL network for consistency between the constant 
CARRIERS and the constraint of the selection restric- 
tion. To see this, consider the case of command (in 
the sense of a military command) which requires that 
its direct object in active clauses be a MILITARY- 
UNIT and that its surface subject in passive clauses 
be a MILITARY-UNIT, i.e., its logical object must be a 
MILITARY-UNIT. Suppose USS Enterprise, carrier, 
and aircraft carrier all have semantic class CARRIER. 
Since an ancestor of CARRIER in the taxonomy is 
MILITARY-UNIT, each of those phrases satisfy the 
aforementioned selection restriction on the verb 
command. Phrases whose class does not have 
MILITARY-UNIT as an ancestor or as a descendent 4 
will not satisfy the selection restriction. That is, 
definite evidence of consistency with the selection 
restriction is normally required. 
Expression Semantic Class 
(THE x P (R x)) P 
(POWER P) P 
(KIND P) P 
(SAMPLE x P (R x)) P 
(LAMBDA x P (R x)) P 
Figure 3: Relating Expressions to Classes s 
There are three cases where more must be done. 
For pronouns, Janus saves selection restrictions that 
would apply to the pronoun's referent, later applying 
those constraints to eliminate candidate referents. 
Metonymy is an exception, discussed in Section 4.3.2. 
There are cases of selection restrictions requiring in- 
formation additional to the semantic class, but these 
are checked against the type of the logical 
expression s for a noun phrase, rather than its seman- 
tic class only. Co/fide requires a set of agents. The 
type of a plural, for instance, is (SET P), where P is its 
semantic class. The selection restriction on collide 
could be represented as (SET PHYSICAL-OBJECT). 
4.1.2. Highly Polysemous Words 
Have, with, and of, are highly polysemous. Some 
of their senses are very specific, frozen, and predict- 
able, e.g., to have a col~ these senses may be 
itemized in the |exicon. However, other senses are 
vague, if considered in a domain-independent way; 
nevertheless, they must be resolved to precise mean- 
ings if accessing a data base, expert system, etc. 
US$ Frederick has a speed of 30 knots has this 
flavor, for the general sense is associating an attribute 
with an entity. 
To handle such cases, we look for a relation R in 
the domain model which could be the domain- 
dependent interpretation. If A has B, the B of A, or ,4 
with B are input, the semantic interpreter looks for a 
role R from the class associated with A to the class 
associated with B. If no such role exists, the search is 
for a role relating the nearest ancestor of the class of 
A to any ancestor of the class of B. The implicit as- 
sumption is that items structured closely together in 
the domain model can be related with such vague 
words, and that items that can be related via such 
vague words will naturally have been organized 
closely together in the domain model. 
While describing the procedure as a search, in 
fact, an explicit run-time search may not be neces- 
sary. All SUPERCs (ancestors) of a concept are com- 
piled and stored when the taxonomy is loaded. All 
roles from one concept to another are also pre- 
compiled and stored, maintaining the distinction be- 
tween roles that are explicit locally versus those that 
are compiled. Furthermore, the ancestors and role 
relations are indexed. One need only walk up the 
chain of ancestors if no locally defined role relates the 
two concepts, but some inherited (not locally defined) 
role does; then one walks up the ancestor chain(s) 
only to find the closest applicable role. Thus, in many 
cases, "semantic reasoning" is reduced to efficient 
table lookup. 
4.1.3. Relation to Underlying System 
Adopting WML offers the potential of simplifying 
the mapping from surface form to semantic represen- 
tation, although it does increase the complexity of 
mapping from WML to executable code, such as SQL 
or expert system function calls. The mapping from 
intensional logic to executable code is beyond the 
scope of this paper; our first implementation was 
reported in \[30\]; the current implementation will be 
described elsewhere. 
This process makes use of a model of underlying 
system capabilities in which each element relates a 
set of domain model constants to a method for ac- 
cessing the related information in the database, ex- 
pert system, simulation program, etc. For example, 
the constant HARPOON-CAPABLE, which defines a 
set of vessels equipped with harpoon missiles, is as- 
sociated with an undedying system model element 
which states how to select the subset of exactly those 
vessels. In a Navy relational data base that we have 
dealt with, the relevant code selects just those records 
of a table of unit characteristics with a "Y" in the 
HARP field. 
~Ne ched~ whether the constraint is a descendent of the class of 
the noun phrase to determine whether consistency is possible. For 
instance, if decom/ssion requires a VESSEL as the object of the 
de<:ommisioning, those units and they satisfy the selection constrainL 
SThe ruJels may need to be used tecureively to get to a constanL 
aEvery expression in WML has a type. 
4.1.¢ Knowledge Acquisition 
We have developed two complementary tools to 
greatly increase our productivity in porting BBN's 
Janus NL understanding and generation system to 
new domains. IRACQ \[3\] supports learning lexical 
semantics from examples with only one unknown 
197 
word. IRACQ is used for acquiring the diverse, com- 
plex patterns of syntax and semantics arising from 
verbs, by providing examples of the verb's usage, 
Since IRACQ assumes that a large vocabulary is 
available for use in the training examples," a way to 
rapidly infer the knowledge bases for the overwhelm- 
ing majority of words is an invaluable complement. 
KNACQ \[33\] serves that purpose. The domain 
model is used to organize, guide, and assist in acquir- 
ing the syntax and semantics of domain-specific 
vocabulary. Using the browsing facilities, graphical 
views, and consistency checker of KREME\[1\] on 
NIKL taxonomies, one may select any concept or role 
for knowledge acquisition. KNACQ presents the user 
with a few questions and menus to elicit the English 
expressions used to refer to. that concept or role. 
To illustrate the kinds of information that must be 
acquired consider the examples in Figure 4. 
The vessel speed of Vinson 
The vessels with speed above 20 knots 
The vessel's speed is 5 knots 
Vinson has speed less than 20 knots 
Its speed 
Which vessels have a CROVL of C3? 
Which vessels are deployed C3? 
Figure 4: Examples for Knowledge Acquisition 
To handle these one would have to acquire infor- 
mation on lexical syntax, lexical semantics, and map- 
ping to expert system structure for all words not in the 
domain-independent dictionary. For purposes of this 
exposition, assume that the words, vessel, speed, 
Vinson, CROVL, C3, and deploy are to be defined. A 
vessel has a speed of 20 knots or a vessel's speed is 
20 knots would be understood from domain- 
independent semantic rules regarding have and be, 
once lexical information for vessel and speed is ac- 
quired. In acquiring the definitions of vessel and 
speed, the system should infer interpretations for 
phrases such as the speed of a vessel, the vessel's 
speed, and the vessel speed. 
Given the current implementation, the required 
knowledge for the words vessel, speed, and CROVL 
is most efficiently acquired using KNACQ; names of 
instances of classes, such as Vinson and C3 are 
automatically inferred from instances; and knowledge 
about deploy and its derivatives would be acquired via 
IRACQ. 
To illustrate this acquistion centered around the 
domain model, consider acquistion centered around 
roles. At~'ibutes are binary relations on classes that 
can be phrased as the <relation> of a <class>. For 
instance, suppose CURRENT-SPEED is a binary 
relation relating vesselis to SPEED, a subclass of 
ONE-D-MEASUREMENT. An attribute treatment is 
the most appropriate, for the speed of a vessel makes 
perfect sense. KNACQ asks the user for one or more 
English phrases associated with this functional role; 
the user response in this case is speed. That answer 
is sufficient to enable the system to understand the 
kernel noun-phrases listed in Figure 5. -Since ONE-D- 
MEASUREMENT is the range of the relation, the 
software knows that statistical operations such as 
average and maximum apply to speed. The lexical 
information inferred is used compositionally with the 
syntactic rules, domain independent semantic rules, 
and other lexical semantic rules. Therefore, the 
generative capacity of the lexical semantic and syn- 
tactic information is linguistically very great, as one 
would require. A small subset of the examples il- 
lustrating this without introducing new domain specific 
lexical items appears in Figure 5. 
KERNEL NOUN PHRASES 
the speed of a vessel 
the vessers speed 
the vessel speed 
RESULTS from COMPOSITIONALITY 
The vessel speed of Vinson 
Vinson has speed 1 
The vessels with a speed of 20 knots 
The vessel's speed is 5 knots 
Vinson has speed less than 20 knots 
Their greatest speed 
Its speed 
Which vessels have speed above 20 knots 
Which vessels have speeds 
Eisenhower has Vinson's speed 
Carriers with speed 20 knots 
Their average speeds 
Figure 5: Attribute Examples 
Some lexicalizations of roles do not fall within the 
attribute category. For these, a more general class of 
regularities is captured by the notion of caseframe 
rules. Suppose we have a role UNIT-OF, relating 
CASREP and MILITARY-UNIT. KNACQ asks the 
user which subset of the following six patterns in 
Figure 6 are appropriate plus the prepositions that are 
appropriate. 
1. <CASREP> is <PREP> <MILITARY-UNIT> 
2. <CASREP> <PREP> <MILITARY-UNIT> 
3. <MILITARY-UNIT> <CASREP> 
4. <MILITARY-UNIT> is <PREP> <CASREP> 
5. <MILITARY-UNIT> <PREP> <CASREP> 
6. <CASREP> <MILITARY-UNIT> 
Figure 6: Patterns for the Caseframe Rules 
For this example, the user would select patterns (1), 
198 
(2), and (3) and select for, on. and of as prepositions. 7 
The information acquired through KNACQ is used 
both by the understanding components and by BBN's 
Spokesman generation components for paraphrasing, 
for providing clarification responses, and for answers 
in English. Mapping from the WML structures to lex- 
ical items is accomplished using rules acquired with 
KNACQ, as well as handcrafted mapping rules for 
lexical items not directly associated with concepts or 
roles. 
4.2. Where an Alternative Mechanism was 
Selected 
Though the domain model is central to the seman- 
tic processing of Janus, we have not used it in all 
possible ways, but only where there seems to be clear 
benefit. 
In telegraphic language, omitted prepositions, as 
in List the creation date file B, may arise. Alter- 
natively, if the NLP system is part of a speech under- 
standing system, prepositions are among the most 
difficult words to recognize reliably. Omitted preposi- 
tions could be treated with the same heuristic as im- 
plemented for interpreting the meaning of have, with, 
and of. However, we have chosen a different in- 
ference technique for omitted prepositions. 
Though one could represent selection restrictions 
directly in a taxonomy (as reported in \[7, 29\]), selec- 
tion restrictions in Janus are stored separately, in- 
dexed by the semantic class of the head word. We 
believe it more likely that Janus will have the selec- 
tional pattern involving the omitted preposition, than 
that the omitted preposition corresponds to a usage 
unknown to Janus and inferable from the domain 
model relations. Consequently, Janus applies the 
selection restrictions corresponding to all senses of 
the known head, to find what senses are consistent 
with the proposed phrase and with what prepositions. 
In practice, this gives rise to far fewer possibilities 
than considering all relations possible whether or not 
they can be expressed with a preposition. 
4.3. Proposals not yet Implemented (Possible 
Future Directions) 
In this section, we speculate regarding some pos- 
sible future work based on further exploiting the 
domain model and hybrid representation system 
described in this paper. 
7Normally, if pattern (1) is valid, pattern (2) will be as well and vice 
versa. Similarly, if pattern (4) is valid, pattern (5) will normally be 
also. As a result, the menu items are coupled by default (selecting 
(1) automatically selects (2) and vice versa), but this default may be 
simply overridden by selecting either and then decelecting the other. 
The most frequent examples where one does not have the coupling 
of these patterns is the preposition of. 
4.3.1. An Approach to Bridging 
It has long been observed \[11 \] that mention of one 
class of entities in a communication can bring into the 
foreground other classes of entities which can be 
referred to though not explicitly introduced. The 
process of inferring the referent when such a refer- 
ence occurs has been called bridging \[12\]. Some ex- 
amples, taken from \[12\], appear below, where the ref- 
erence requiring bridging is underlined. 
1. I looked into the room. The ceilinq was very 
high. 
2. I walked into the room. The chandeliers 
sparkled brightly. 
3. I went shopping yesterday. The time I started 
was 3 PM. 
We believe a taxonomic domain model provides 
the basis for an efficient algorithm for a broad class of 
examples of bridging, though we do not believe that it 
will cover all cases. If A is the class of a discourse 
entity arising from previous utterances, then any entity 
of class B, such that the NIKL domain model has a 
role from A to B (or from B to A) can be referred to by 
a definite NP. This has not yet been integrated into 
the Janus model of reference processing \[4\]. 
4.3.2. Metonymy 
Unstated relations in a communication must be 
inferred for full understanding of nominal compounds 
and metonymy. Those that can be anticipated can be 
built into the lexicon; the challenge is to deal with 
those that are novel to Janus. Finding the omitted 
relation in novel nominal compounds using a 
taxonomy has been explored and reported elsewhere 
\[13\]. 
We propose treating many novel cases of 
metonymy in the following way: 
1. Wherepatterns of metonymy can be identified,, 
such as using a description of a part to refer to 
the whole (and other patterns identified in 
\[17\]), pro-compile chains of relations between 
classes in the domain model, e.g., (PART-OF 
A B) where A and B are concepts. 
2. In processing an input, when a selection 
restriction on an NP fails, record the failed 
restriction with the partial interpretation for 
possible future processing, after all attempts at 
a literal interpretation of the input have failed. 
3. If no literal interpretation of the input can be 
found, look among the precompiled relations 
of step 1 above for any class that could be so 
related to the class of the NP that appears. 
4. If a relation is applicable, attempt to resume 
interpretation assuming the referent of the NP 
is in the related class. 
This has not been implemented, but offers an efficient 
199 
alternative to the abductive theorem-proving approach 
described in \[16\]. 
5. Top-Level Abstractions in the NIKL 
Taxonomy 
WML and NIKL together provide a framework for 
representation. The highest concepts and relations in 
the NIKL network provide a representational style in 
which more concrete constantsmust fit. The first 
abstraction structure used in Janus was the USC/ISI 
"upper structure" \[19\]. Because it seemed tied to sys- 
temic linguistics in critical ways, rather than to a more 
general ontological style, we have replaced it with 
another domain-independent set of concepts and 
roles. For any application domain, all domain- 
dependent constants must fit underneath the domain- 
independent structure. The domain-independent 
taxonomy consists of 70 concepts and 24 roles cur- 
rently, but certainly could be further expanded as one 
attempts to further axiomatize and model notions use- 
ful in a broad class of application domains. 
During the evolution of Janus, we explored 
whether the domain-independent taxonomy could be 
greatly expanded by a broad set of primitives used in 
the Longman Dictionary of Contemporary English 
\[18\] (LDOCE) to define domain-independent con- 
stants. LDOCE defines approximately 56,000 words 
in terms of a base vocabulary of roughly 2,000 items, s 
We estimate that about 20,000 concepts and roles 
should be defined corresponding to the 2,000 multi- 
way ambiguous words in the base vocabulary. The 
appeal, of course, is that if these basic notions were 
sufficient to define 56,000 words, they are generally 
applicable, providing a candidate for general-purpose 
primitives. 
The course of action we followed was to build a 
taxonomy for all of the definitions of approximately 
200 items from the base vocabulary using the defini. 
tJons of those vocabulary items themselves in the 
dictionary. In this attempt, we encountered the follow- 
ing difficulties: 
• Definitions of the base vocabulary often in- 
volved circularity. 
• Definitions included assertional information 
and/or knowledge appropriate in defeasible 
reasoning, which are not fully supported by 
NIKL. For example, the first definition of cat is 
"a small four-legged animal with soft fur and 
sharp claws, often kept as a pet or for catching 
mice or rats." 
• Multiple views and/or vague definitions and 
usage arose in LDOCE. For instance, the 
e'rhough the authors of LDOCE definitions try to stay within the 
base vocabulary, exceptions do arise such as diagrams and proper 
nouns, e.g., Catholic Church. 
second definition of cat (p. 150) is "an animal 
related to this such as the lion or tiger" (italics 
added). Such a vague definition helped us little 
in axiomatizing the notion. 
Thus, we decided that hand-crafted abstractions 
would be needed to axiomatize by hand the LDOCE 
base vocabulary if general-purpose primitives were to 
result. On the other hand, concrete concepts cor- 
responding to a lower level of abstraction seem ob- 
tainable from LDOCE. In particular the LDOCE defini- 
tions of units of measurement for the avoirdupois and 
metric systems were very useful. A more detailed 
analysis of our experience is presented in \[23\]. 
6. Related Work 
Several hybrid representation schemes have been 
created, although only ours seems to have explored a 
hybrid of intensional logic with an axiomatizable frame 
system. The most directly related efforts are the fol- 
lowing: 
• KL-TWO\[31\], which marries a frame system 
(NIKL) with propositional logic (RUP\[20\]), 
Limited inference in propositional logic is the 
goal of KL-'FWO. Limited aspects of universal" 
quantification are achieved via allowing demons 
in the inference process. KL-TWO and its clas- 
sification algorithm \[27\] are at the heart of the 
lexicalization process of the text generator Pen- 
man \[28\]. 
• KRYPTON \[9\], which marries a frame system 
with first-order logic. The frame system is 
designed to be less expressive than NIKL to 
allow rapid checking for disjointness of two 
class concepts in order to support efficient 
resolution theorem proving. KRYPTON has not 
as yet been used in any natural language 
processor. 
7. Conclusions 
Our conclusions regarding the hybrid represen- 
tation approach of intensional logic plus NIKL-based 
axioms to define constants are based on three kinds 
of efforts: 
• Bringing Janus up on two large expert system 
and data base applications within DARPA's 
Battle Management Programs. The combined 
lexicon in the effort is approximately 7,000 
words (not counting morphological variations). 
• The efforts synopsized in Section 5 towards 
general purpose domain notions. 
• Experience in developing IRACQ and KNACQ, 
acquisition tools integrated with the domain 
model acquisition and maintenance facility 
KREME, 
200 
First, a taxonomic language with a formal seman- 
tics can supplement a higher order logic in support of 
efficient, limited inferences needed in a naturaJ lan- 
guage processor. Based on our experience and that 
of others, the axioms and limited inference algorithms 
can be used for classes of anaphora resolution, inter- 
pretation of have, with, and of, finding omitted rela- 
tions in novel nominal compounds, applying selection 
restrictions, and mapping from the semantic represen- 
tation of the input to code to carry out the user's re- 
quest. 
Second, an intensional logic can supplement a 
taxonomic language in trying to define word senses 
formally. Our effort with LDOCE definitions showed 
how little support is provided for defining word senses 
in a taxonomic language. A positive contribution of 
intensional logic is the ability to distinguish universal 
statements from generic ones from existential ones; 
definite sets from unspecified ones; and necessary 
and sufficient information from assertional information, 
allowing for a representation closer to the semantics 
of English. 
Third, the hybridization of axioms for taxonomic 
knowledge with an intensional logic does not allow us 
to represent all that we would like to, but does provide 
a very effective engineering approach. Out of 7,000 
lexical entries (not counting morphological variations), 
only 0.1% represented concepts inappropriate for the 
formal semantics of NIKL. 
The ability to pre-compile pre-specified, inferential 
chains, to index them via concept name and role 
name, and to employ taxonomic inheritance for or- 
ganizing knowledge were critical in selecting 
taxor~omic representation to supplement WML. These 
techniques of pre-compiling pre-specified inferential 
chains and of indexing them should also be applicable 
to other knowledge representations than taxonomies. 
At a later date, we hope to quantify the effec- 
tiveness of the semantic heuristics described in this 
paper. 
Acknowledgements 
This research was supported by the Advanced 
Research Projects Agency of the Department of 
Defense and was monitored by ONR under Contracts 
N00014-85-C-0079 and N00014-85-C-0016. The 
views and conclusions contained in this document are 
those of the author and should not be interpreted as 
necessarily representing the official policies, either ex- 
pressed or implied, of the Defense Advanced 
Research Projects Agency or the U.S. Government. 
This brief report represents a total team effort. 
Significant contributions were made by Damaris 
Ayuso, Rusty Bobrow, Ira Haimowitz, Erhard Hinrichs, 
Thomas Reinhardt, Remko Scha, David Stallard, and 
Cynthia Whipple. We also wish to acknowledge many 
discussions with William Mann and Norman 
Sondheimer in the early phases of the project. 
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