THE SUBWORLD CONCEPT LEXICON AND THE LEXICON MANAGEMENT 
SYSTEM 
Sergei Nirenburg 
Center for Machine Translation 
Carnegie Mellon University 
Victor Raskin 
Natural Language Processing Laboratory 
Purdue University 
Natural language processing systems require three different types of lexicons: the concept lexicon that 
describes the (sub)world ontology and the analysis and generation lexicons for natural languages. We 
argue that the acquisition of the concept lexicon must precede any lexical work on natural language and 
that a comprehensive lexicon management system (LMS) is necessary for lexicon acquisition in 
large-scale applications. We describe the interactive concept lexicon acquisition module of the LMS for 
TRANSLATOR, a knowledge-based, sublanguage-oriented machine translation project. 
This project belongs to two of the fastest growing fields 
of computational linguistics and artificial intelligence in 
general: the lexicon and knowledge acquisition for AI 
systems. 
The work in lexicon has centered on a) studies 
concerned with the utilization of conventional human- 
oriented dictionaries, newly available in machine-read- 
able form, for computational tasks (e.g., Amsler 
1984a,b; Chodorow et al. 1985; Ahlswede 1985; Marko- 
witz et al. 1986) improving the ancillary capabilities for 
lexicon systems, such as, for instance, morphological 
processors and descriptions (e.g., Nirenburg and Ben 
Asher 1984; Byrd et al. 1986; Boguraev et al. 1987); c) 
hand-building of lexicons necessary for natural language 
systems, often with considerations about extensibility 
(e.g., Zernik and Dyer 1985; Bessemer and Jacobs 
1987). An interesting perspective on the field is given in 
Miller (1985). 
Knowledge acquisition is a central topic in AI and 
expert systems. A number of systems exist for assis- 
tance in acquiring specialized knowledge for natural 
language processing. Thus, TEAM (Grosz et al. 1985), 
TEL1 (Ballard and Stumberger, 1986), IRACQ (Ayuso 
et al. 1987) and some others are devoted to facilitating 
customizations in the framework of database query 
systems; INKA (Phillips et al. 1986) and CYC (Lenat et 
al. 1986) are examples of knowledge acquisition systems 
aimed at expert system design. 
We would like to address some important method- 
ological and strategic points in providing the lexicon 
support in the context of a text processing system, such 
as the machine translation system TRANSLATOR (Ni- 
renburg et al. 1987). 
1. NEED HELP BUILDING LEXICON 
A natural language lexicon is a necessary part of any 
natural language processing (NLP) system. Building 
such a lexicon is an important Rnowledge acquisition 
task. The format of the lexicon has been the subject of 
substantial number of research projects, both in linguis- 
tics and in artificial intelligence (AI). In linguistic se- 
mantics, the efforts have focused on the formal notation 
for representing semantic knowledge with a special 
emphasis on the involved categories and rules. The 
projects have ranged from simple feature notations 
(Katz, Fodor 1963; Bendix 1966) to script-based con- 
textual analysis (Raskin 1986). 
In AI, this type of work belongs to the area of 
knowledge representation. A number of methods of 
representing knowledge have been developed, ranging 
from first-order predicate calculus notation to quite 
sophisticated frame-based and advanced logical formal- 
isms. (Brachman and Levesque 1985 summarizes the 
state of the art in knowledge representation.) 
By comparison, the number of projects devoted to 
the design of procedures for compiling and maintaining 
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276 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
a large lexicon has been relatively small. The usual 
practice in AI has been to suggest a relatively well- 
developed knowledge representation language and then 
use it to represent a miniature world or a very restricted 
sublanguage. It is clear, however, that for an NLP 
system to be really useful, one needs not only to provide 
a language in which to record the meanings of linguistic 
units, but also, in fact, to record those meanings for a 
non-miniature world (and its corresponding sublangu- 
age). 
The crux of this problem is generating linguistic 
heuristics to support the growth of the representation of 
the subworld/sublanguage. Indeed, there can be no 
deterministic solution to the problem of knowledge 
• acquisition; therefore, weaker, heuristic methods 
should be applied to (a) discovering the categories in 
terms of which to account for the morphological, syn- 
tactical, and semantical properties of words and phrases 
of natural language and (b) developing guidelines for 
compiling the lexicon for a natural language. It follows 
from the above that new, sophisticated ways of human- 
computer interaction must be devised in order to sup- 
port the tasks of linguistic description in terms of 
AI-style knowledge representation. 
The implication of many workers in knowledge rep- 
resentation that once the notation is developed it will be 
easy to use it for actually describing the world, is clearly 
anti-intuitive and anti-experiential, as anybody who has 
ever developed an NLP system knows only too well. 
Quite frequently, the lack of any reference to the 
procedures of lexical description spells out the differ- 
ence between a system conceived and a system exe- 
cuted. 
Thus, decisions to assign a word to a postulated 
category, for instance, are far from straightforward -- 
many borderline cases have to be routinely considered 
and resolved. In any large application, lexicon building 
is typically done by a large number of relatively un- 
trained people who are certain to make non-uniform 
decisions in many cases. The instructions these people 
obtain at the beginning of their work cannot be very 
precise, unambiguous, or designed to provide in ad- 
vance for any contingency. As a result, even a good 
knowledge representation language cannot guarantee a 
quality lexicon. 
One can in principle think of avoiding these diffi- 
culties through automation. Fully automated lexicon 
building procedures would definitely ensure the unifor- 
mity of description. There was a considerable enthusi- 
asm about the possibility of a complete automation of 
lexicology some 15-20 years ago (see, for instance, 
Collin 1960; Grimes 1970; Kucera 1969; Venezky 1973). 
A more feasible scenario, however, is partial computa- 
tional assistance for lexicology, probably along the lines 
suggested in this paper, as well as utilizing such new and 
promising resources as on-line dictionaries. 
Recent work on machine-readable dictionaries offers 
new and interesting possibilities both for the computer- 
assisted lexicology (see Walker 1984) and for construct- 
ing lexical databases derived from the definitions in 
machine-readable dictionaries and utilized in NLP along 
with other fields (see Amsler 1982, 1984a; Walker, 
Amsler 1986, Calzolari 1984a,b). The premises and 
goals of these efforts are fully compatible with our 
belief, first, that no AI system is ready to make the kind 
of decisions that lexicon building requires and, second, 
that 'simply having an online version of an encyclopedia 
\[or a dictionary\] would be of little use, as there is 
practically nothing that current AI could draw from the 
raw text. Rather, we must carefully re-represent the 
encyclopedia's knowledge -- by hand -- into some 
more structured form' (Lenat et al. 1986:75). Such 
re-representation would be necessary for Amsler's 
(1984a:458) 'lexical knowledge base \[which\] is a repos- 
itory of computational information about concepts' and 
which contains information derived from machine-read- 
able dictionaries, the full text of reference books, the 
results of statistical analysis of text usages, and data 
manually obtained from human world knowledge.' 
This paper deals primarily with the last item on 
Amsler's agenda. It is based on the following approach 
to the problem of lexicon building. The work is done by 
humans assisted by an interactive aid which enhances 
productivity and ensures uniformity. It is important to 
recognize that lexicon building in NLP involves the 
acquisition of not one entity but rather of three interre- 
lated but distinct lexicons, namely 
a) the world concept lexicon which structures our 
knowledge of the world 
b) the analysis lexicon which is indexed by natural 
language words and phrases connected with concepts 
from the world concept lexicon, and 
c) the generation lexicon, which is indexed by con- 
cepts in the world concept lexicon connected with 
natural language words and phrases. 
In reality, AI systems deal not with the entire world 
but typically with a well-specified subworld of it. In 
other words, in any practical application, the world 
concept lexicon will, in fact, be a subworld concept 
lexicon. In the area of machine translation the analysis 
and generation lexicons involve two different natural 
languages, which creates the task of building such 
resources as, say, an English- subworld and subworld 
-- Russian lexicons, as is the case, for example, in 
TRANSLATOR, the knowledge-based machine trans- 
lation project for the computer subworld (see Nirenburg 
et al. 1985, 1986, 1987). 
2. WORLD FIRST, WORD LATER 
We assert that of the three lexicons described above, 
the first to be built must be the subworld concept 
lexicon. The availability of such a lexicon is a sine qua 
non for any subsequent lexical work in NLP. 
The recognized necessity to describe the concept 
lexicon prior to dealing with natural languages is a 
feature which clearly distinguishes our approach from 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 277 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
other work on lexical aids (e.g., Ahlswede 1985) and 
brings us closer to some non-NL work on knowledge 
acquisition (e.g., Lenat et al. 1986). 
Ahlswede (1985) describes a typical approach to 
lexicon building in a constrained subworld. The sub- 
world is that of stroke medicine. The interactive aid is 
designed for the analysis lexicon, in our terminology. 
The concept realm is not discussed at all, though the 
four major semantic features used in this lexicon are 
obviously underlain by subworld concepts. As a result, 
the structure and interrelations of concepts cannot be 
conveniently reasoned about. Thus, for instance, the 
distinction between 'general' and 'specific' features is 
not supported by the ontology and is not really used, 
since only one subworld is chosen. 
We agree with those who believe that each subworld 
must be described fully and uniformly prior to the 
introduction of any semantic feature. This may not be 
feasible or practical for some very rich domains -- it is 
definitely beyond reach as far as the whole world served 
by the whole language is concerned. However, most 
NLP systems are designed for constrained domains, 
and it will probably remain this way for some time. 
The description of a subworld is a matter of distin- 
guishing all the relevant concepts in it and arranging 
them in a logical, consistent, and meaningful way. We 
agree entirely with Lenat et al. (1986:75) that to create 
a knowledge acquisition system, 'we must encode all 
the world's knowledge down to some level of detail; 
there is no way to finesse this.' We differ from them by 
our overt, theoretically built-in emphasis on limited 
subworlds. More importantly, we want to assist the 
'knowledge enterers' with an interactive aid it is not at 
all clear how the CYC system of knowledge acquisition 
which Lenat et al. describe can ensure the quality and 
uniformity of the descriptions without such a device. 
Another reason why it is important as well as conve- 
nient to precede the lexical work with the description of 
the subworld is that such a description is natural lan- 
guage-independent. In other words, the same subworld 
may correspond to sublanguages of different natural 
languages. In MT, the representation of the subworld 
functions as -- and is -- the interlingua. Beginning 
directly with the lexicon of a natural language would 
make the implied subworld biased toward this language, 
and this is known to have an adverse affect on the 
description of other natural sublanguages corresponding 
to the same subworld. 
The subworld concept lexicon determines the struc- 
ture of the associated analysis and generation lexicons. 
Thus, the constrained nature of the subworld (see 
Raskin 1985 and references therein as well as Kittredge 
and Lehrberger 1982 and Grishman and Kittredge 1986) 
always severely limits polysemy/homonymy so rampant 
in the language as a whole. Thus, the concept lexicon 
for the computer subworld will make it clear that there 
is a need to accommodate only meanings (5) and (7) in 
the lexical entry for operator in the corresponding 
sublanguage of English, even though the English lan- 
guage possesses all of the meanings listed in Figure I 
(cf. SOED 1973:1453). 
(1) one who does something, an agent 
(2) one who is professionally engaged to perform a 
certain operation 
(3) a surgeon 
(4) one who carries on financial operations 
(5) one who works a machine 
(6) one who works a business 
(7) a symbol 
Figure 1. Meanings of operator 
In fact, (5) will be narrowed down even further in the 
sublanguage, to 'one who works a computer' and this is 
what the English (or any other natural language) lexical 
entry should be limited to. 
The subworld concept lexicon also determines, to a 
large extent, the inventory of semantic features used in 
the entries of the analysis and generation lexicons, the 
values of those features, and their status. Thus, a binary 
semantic feature is usually induced by a branching in 
the 'isa' hierarchy underlying the concept lexicon. 
Frame slots are suggested by a perceived link between 
some two hierarchically unrelated concepts in the con- 
cept lexicon. The relative importance and scope of 
features used in analysis and generation lexicons de- 
pends on the status of the corresponding concepts in the 
concept lexicon. We now rest our case for the priority 
of the concept lexicon over the analysis and generation 
lexicons and proceed to describe a system for facilitat- 
ing lexicon acquisition and maintenance. 
2.1 ThE DESIGN OF A LEXlCON MANAGEMENT 
SYSTEM. 
In a large-scale NLP application, the process of acquir- 
ing and maintaining the various lexicons cannot be left 
unattended. Therefore, any such application requires a 
lexicon management system (LMS). Historically, rela- 
tively less attention has been devoted to the lexicons in 
this context than to either the grammars or the actual 
processing modules -- parsers, inference engines, and 
generators. The importance of an LMS grows propor- 
tionally to the size of the lexicons necessary for an 
application and also to the depth of coverage. Thus, for 
instance, it is extremely important to use a principled 
LMS in a knowledge-based machine translation system 
where the lexicons must cover not only the usual 
morphological and syntactic knowledge but also the 
semantic and pragmatic knowledge about the transla- 
tion area and where the typical number of concepts in 
such an area is large. 
An LMS is a collection of programs that help create, 
augment, modify, and test the various lexicons in an 
NLP application. The particular LMS described in this 
paper is suggested for the TRANSLATOR project. The 
goal of TRANSLATOR is ultimately to develop a 
knowledge-based multilingual machine translation sys- 
278 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
I Subworld Subworld 
Concept Concept 
I Lexicon Lexicon 
World 
Concept 
Lexicon // 
Analysis Analysis Lexicon Lexicon 
(Natural (Natural 
Language A) Language B) 
Subworld 
Concept Lexicon -< // 
1 
Generation Generauon 
Lexicon Lexicon 
(Natural (Natural 
Language C) Language A) 
Knowledge 
I | Representation 
<---2 Language 
Figure 2. The architecture of the TRANSLATOR Lexicon Module, including the Lexicon Management System (LMS). At 
present, the world concept lexicon is identical with the only subworld concept lexicon--that of computer science. 
tem for multiple subject areas. Various modules in the 
system are designed so as to allow interactive human 
participation with no pre- or post-editing. The LMS is 
one such module. 
The lexicon component of TRANSLATOR (Figure 
2) contains four types of lexicons: 
1. the subworld concept lexicons, which contain 
representations, in the DRL knowledge represen- 
tation language (Nirenburg, Raskin, Tucker 1986), 
of concepts that belong to a particular subworld, 
such as, for instance, the subworld of weather 
reports or of research papers in computer science; 
2. the world concept lexicon, in which the informa- 
tion from all the available subworld concept lexi- 
cons is merged; 
3. the analysis lexicons, whose entries are indexed by 
lexical units in a natural language and which 
contain 
a) syntactic information about these units; 
b) a pointer to a corresponding concept in the con- 
cept lexicon (this link essentially assigns semantic 
meaning to the lexical unit; note that some lexical 
units do not correspond to concepts); and/or 
c) additional control and constraint information for 
the use of the analysis module of the underlying 
NLP system. 
4. the generation lexicons, whose entries are indexed 
by concepts in the concept lexicon and which contain 
pointers to corresponding lexical units in the target 
language. (Note that the analysis and generation lexi- 
cons are not symmetrical; for a detailed analysis of the 
differences see Nirenburg and Raskin 1989.) 
LMS maintains all four types of lexicons. In this 
paper we will concentrate on describing that part of the 
LMS which is devoted to the maintenance of one 
subworld concept lexicon (that of computer science 
research). 
The primary purpose of an LMS at the first stage of 
the project is to support knowledge acquisition. At later 
stages in the life of the LMS, testing and modification 
will become the primary types of work it supports. An 
ordinary LMS user, i.e., an enterer, will obtain at this 
stage a list of concepts to enter in the concept lexicon 
and will code the information about them in DRL. The 
LMS assists the enterer by providing graphic and other 
aids for human decision making. In case of doubt, the 
enterer can try to resolve the difficulty or to refer it to 
the lexicon manager, whose responsibility it is to force 
solutions to problems in lexicon acquisition. 
The task of the manager has much in common with 
that of a database administrator in the database system 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 279 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
environment. In their respective capacities, they are 
both responsible for 
a) maintaining the format and contents of the knowl- 
edge representation language ('data dictionary' in 
database terms); 
b) defining and executing the security and integrity 
checks for the accumulated data; 
c) developing and running statistic analysis routines 
to monitor access time, etc. (this becomes impor- 
tant in production-size lexicon systems); 
d) interfacing with regular users (enterers for the 
lexicon manager). 
In addition to the above, the lexicon manager also 
modifies the knowledge representation language in ac- 
cordance with the evidence accumulated in the process 
of knowledge acquisition. 
The LMS thus has two modes of operation: a mode 
for enterers and a mode that supports the activities of a 
lexicon manager. In what follows we will describe and 
illustrate how these modes are implemented in the 
TRANSLATOR LMS. The system has been imple- 
mented in Zetalisp on a Symbolics 3600 Lisp Machine. 
2.2 A DRL ONTOLOGY FOR COMPUTER SUBWORLD 
The intended result of the first stage of research re- 
ported in this paper is a computer subworld concept 
lexicon. The subsequent stages will add a number of 
analysis and generation lexicons. We have argued that 
for the purpose of lexicon building it is necessary to go 
beyond just proposing a knowledge representation 
framework. It is necessary to use it for the construction 
of actual lexicons and to keep using it until the lexicons 
are judged sufficiently complete. An LMS must contain 
a set of interactive aids to facilitate this type of activity. 
The first step is, however, still to suggest a knowl- 
edge representation scheme for coding lexical meanings 
in a concept lexicon. Structurally, the concept lexicon 
can be viewed as a complex network, with concepts as 
nodes. The connections in this network place the nodes 
in various hierarchies and classify them on the basis of 
certain characteristics and constraints. We suggest a 
frame-based representation in which frames correspond 
to concepts, and slots convey constraints on the mean- 
ing of these concepts. The sets of values that can 
occupy certain slots, the domains of the latter, can be 
further classified. Thus, some slots take names of 
concepts in the world as values (such are hierarchy- 
related slots); some others, take values from specially 
defined property sets. The slots can be occupied by any 
number of members of the corresponding domain, and 
the logical operators and, or, and not can be used to 
augment the expressive power. Also, in every case, the 
semantics of the constraints in the lexicon is that of 
default knowledge: the contents of a slot are understood 
as typically constraining the meaning of the concept. 
The semantic character of a slot in a concept lexicon 
frame underscores a careful distinction that should be 
made between concept types and concept tokens. Such 
a distinction is common in certain knowledge represen- 
tation languages (see Brachman and Schmolze 1985; 
Nirenburg et al. 1986). Concept types belong in the 
lexicon; concept tokens are instantiations of concept 
types obtained as a result of analyzing natural language 
inputs. One kind of knowledge representation can be 
used for both types and tokens, a frame-based notation 
being one of them. (Of course, due to a number of 
possible reasons, a complete knowledge representation 
system can use one type of representation for the types 
and another for the tokens, cf. Hobbs 1985 for a 
discussion of' such a position.) The frames for a type and 
its token will not, however, be identical in structure. 
The semantic content of slots in a lexicon (type) frame 
is different fi:om that of the corresponding slots in the 
text (token) frame. Concept tokens have their slots 
occupied by actual values of properties; if information 
about a property is not forthcoming, then the default 
value (if any) is inherited from the corresponding type 
representations. For example, the frame for a verbal 
action type and a verbal action token can have a slot 
named 'agent.' However, in the former case, the slot 
can be occupied by a concept type, such as, for in- 
stance, 'human.' In the latter case, the slot must be 
occupied by a concept token, such as 'John23.' If the 
analysis program cannot make a decision as to what 
token(s) must occupy a slot, it produces a temporary 
filler token where constraints will be inherited from its 
corresponding type. In this case, it may be 'personl,' 
with all the constraints of the type 'human' inherited. 
The concept lexicon forms a tangled ISA hierarchy 
with property inheritance. However, the examples be- 
low are simplified to make it look as a strict hierarchy. 
The current version of the top levels of this hierarchy is 
shown in Figure 3. The actual world ontology in a 
working system depends on the subworld for which it is 
developed. We will survey the concept frames in the 
order suggested by the ISA hierarchy. 
all:: = (all 
(id string) 
(subworld subworld*)) 
This is the root of the isa hierarchy. The two slots 
mean that every node has an id and represents a concept 
that belongs to one or more subworlds. 
process ::= (process 
(isa all) 
(patient object)) 
At this level we meet the 'isa' slot, the pointer to a 
node's parent in the hierarchy. Processes, as one can 
see from Figure 3, divide into actions and states. The 
only overtly mentioned property common to all pro- 
cesses is the conceptual case of 'patient' (this reflects 
our opinion that in the English sentence 'John is asleep' 
John is not an agent, but rather a patient). Note that 
'patient' subsumes the semantics of 'beneficiary.' 
action ::= (action 
(isa process) 
(consists-of process-sequence) 
280 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
~ plant 
/ - olive ~ container 
/ ~ artifact ~ letter 
t object. ~ ' 
• -- ~'- ~vehicle 
I --mental-object J jdata----datobase 
all/ ~ infor moti°n ~ program ~~" :doim~tiler 
\  physical-s,ate 
/ reaction 
\process menial oc'tion Jcognition \ J "  oe,=,oo 
\oc,,ooJ ~ossertion speech -aclion ~"-request ~ req- into 
~req -action  move 
physical- action ingest 
~operate 
Figure 3. A fragment of the ISA hierarchy for the computer science world. 
(part-of process*) 
(preconditions state*) 
(effects state*) 
(tempor process*) 
(agent creature) 
(object all) 
(instrument object) 
(source object) 
(destination object)) 
The action frame contains two groups of slots: 
a) paradigmatic relation slots (isa, consists-of, part- 
of, preconditions, effects, tempor) that connect it 
with other processes; 
b) syntagmatic relation slots (the conceptual case 
slots agent, object, instrument, source and desti- 
nation; patient is inherited). 
The consists-of slot contains either the constant 
'primitive,' if the action is not further decomposable, or 
a description of the sequence of actions which comprise 
the given action. This sequence is a list of action names 
connected by the operators sequential, choice and 
shuffle. In other words, an action can be a sequence of 
subactions ('sequential'), a choice among several subac- 
tions ('choice'), a temporally unordered sequence of 
subactions ('shuffle') or any recursive combination of 
the above. This treatment of processes is inspired by 
Nirenburg, Nirenburg, Reynolds 1985. The tempor slot 
connects the action with other actions that typically 
occur proximally in time to it. Metaphorically, while 
consists-of describes children, tempor describes sib- 
lings. 
physical-action ::= (physical-action 
(isa action) 
(object physical-object)) 
mental-action ::= (mental-action 
(isa action)) 
Only creatures can be fillers for the agent slot. 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 281 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
Mental actions further classify into reaction (cf. the 
English please or like), cognition (deduce) and percep- 
tion actions (see). 
speech-action :: = (speech-action 
(isa process) 
(consists-of primitive) 
(agent person) 
(patient person* \[ organization*) 
(object event) 
(source agent) 
(destination patient)) 
Speech processes are 'primitives'• The agent slot 
filler has the semantics of the speaker. The patient is the 
hearer. 
state ::= (state 
(isa process) 
(part-of state*)) 
The actant in states, which is the patient rather than 
the agent, is inherited from the process frame• 
object :: = (object 
(isa all) 
(part-of object*) 
(consists-of object*) 
(belongs-to creature \[ organization) 
(spatial physical-object*) 
(object-of mental-action speech-action) 
(patient-of process) 
(instrument-of process) 
(source-of process) 
(destination-of process)) 
Once again two groups of slots describe paradigmatic 
and syntagmatic relations. The spatial slot lists objects 
that typically appear proximally in space to the current 
object• The . . .-of slots have the semantics of 'typical 
•..-of' and are complementary to the conceptual case 
slots in process descriptions. 
The other type of slot fillers in the DRL frames is a 
set of property values. Properties are defined in the 
world, each with a corresponding set (domain) of prop- 
erty values. Only an illustration of property values is 
given here. Many more exist and are used in the lexicon 
description. 
size-set :: = (infinitesimal . . . huge) 
color-set :: = (black . . . white) 
shape-set :: = (flat square spherical . . .) 
subworld-set ::= (computer-world business-world 
everyday-world . . .) 
boolean-set ::= (yes no) 
texture-set ::= (smooth . . . rough) 
A path of concepts from the root to a leaf node in the 
isa hierarchy is presented below and followed by the 
corresponding frames (the frames for all and object are 
given above). 
aU-->object--~physical- 
object--->+alive-~creature ~ ~person--~computer-user 
physical-object ::-- (physical-object 
(isa object) 
(object-of (+ (take put)) 
(size size-set) 
(shape shape-set) 
(color color-set) 
(mass integer)) 
The '+' sign in slots means all inherited information 
plus the contents of the current slot. 
+alive ::= (+alive 
(isa physical-object) 
(edibility boolean-set)) 
creature :::= (creature 
(isa +alive) 
(agent-of (eat ingest 
attack)) 
(consists-of(head body)) 
(object-of (+ (attack)) 
(power power-set) 
(speed speed-set)) 
drink move 
person :: :-- (person 
(isa creature) 
(agent-of (+ (take put find speech-action 
mental- 
action))) 
(source-of speech-action) 
(destination-of speech-action) 
(consists-of(+ (hand foot . . .))) 
(power human) 
(speed slow) 
(mass human)) 
computer-user ::= (computer-user 
(isa person) 
(agent-of (+ operate)) 
(subworld computer-world)) 
The complete frame of the leaf of this path, 'computer- 
user,' including all inherited slots and default values, is 
listed below. In reality frames like this do not exist, 
because the tokens of this type do not contain all the 
possible slot fillers• 
(computer-user 
(isa person) 
(consists-of (hand foot head body)) 
( belongs-to none) 
(part-of organization*) 
(spatial physical-object*) 
(agent-of (operate take put find speech-action 
mental-action eat ingest drink move attack)) 
(object-of (find mental-action speech-action at- 
tack take put)) 
(destination-of speech-action) 
(source-of speech-action) 
(power human) 
(speed slow) 
(mass human) 
(subworld computer-world)) 
282 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
jjlJ   
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COMMEND: 
insert 
modifg-tree 
modifg-node 
delete 
: (lexl 
con) 
COMPUTER-USER's attrlbuten: 
IBR person 
COHSISTS-OF hand foot head body 
BELONGS-TO - 
PERT-OF orBantzatton 
SPRTIRL physical-obJect 
ROEMT-OF take put find speech-action mental-action eat ingest drink nave attack 
OBJECT-OF find mental-action speech-action attack take put 
DESTIHRTIOH-OF speech-action 
SOURCE-OF speech-action 
POWER human 
SPEED e|ow 
MRSS human 
3o It \[\] Rbort \[\] 
RETAIN MODIFY \[\] \[\] 
\[\] \[\] \[\] \[\] 
\[\] \[\] 
\[\] o \[\] \[\] 
\[\] \[\] \[\] 
\[\] \[\] \[\] \[\] 
\[\] \[\] 
Figure 4. Entering computer-user, I. 
3. Two SAMPLE SESSIONS WITH THE SUBWORLD 
MODULE OF THE TRANSLATOR LMS 
We will demonstrate the work of the subworld module 
of the TRANSLATOR LMS on two examples. They 
will illustrate the two modes discussed in 2.1, the 
enterer and manager modes. The first, simple example 
will involve the placing of a concept in an appropriate 
slot of the 'isa' tree and checking the slots of the 
inherited frame for appropriate values. All of these 
operations will be executed in the enterer mode. The 
second, more complicated example will present serious 
difficulties for the enterer and will require the manager's 
full authority for the resolution of the problem. In other 
words, the first example will leave the existing ontology 
intact while the second will require its modification. 
3.1 THE ENTERER MODE: ADDING THE CONCEPT 
COMPUTER-USER. 
This concept represents meaning (5) in the dictionary 
entry for the English word operator (see Figure 1). The 
task of the enterer essentially involves the following 
operations: 
Insert: finding the appropriate slot in the isa hierar- 
chy to which the new concept will be attached as a leaf 
child 
Fill: describing all the properties of this concept by 
modifying, wherever necessary, the slot fillers of the 
inherited frame 
Check: checking whether the property values chosen 
distinguish it from its parent and siblings. 
The LMS helps the enterer with air three of these 
activities. To decide on the position of the concept in 
the hierarchy, the enterer can browse through its graph- 
ical representation and view the frames corresponding 
to any node (concept) in it. If the enterer has to consider 
more than one possibility, the frames for several candi- 
date parents for the node to be inserted, can be dis- 
played -- in the current implementation up to three 
candidate frames can be displayed simultaneously. In 
the case of computer-user the enterer does not have a 
difficulty in selecting person as the only candidate 
parent node. 
When the place for a concept is found, a menu 
containing all slots pertinent for the parent node frame 
which incorporates all the slots and slot fillers it inher- 
ited from its ancestors, appears on the screen. The 
enterer scans the menu and modifies, wherever neces- 
sary, the values in these slots. Figure 4 ~ shows the state 
of the process for entering computer-user when the 
values for the agent-of slot are being considered. Note 
that the system provides additional help by listing 
candidate ~,alues for this slot. The set of candidates is 
produced automatically by determining the common 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 283 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
OBJECT 
PHYSICRL-OBJECI MENTAL--OBJECT 
+ALIVE -ALIVE MATERIAL INFORMATION 
RNIBRL PERSON DEVICE COHTRIBER OOCOBEBT EDITOR conPILER DONS DATABASE 
VEHICLE COMPUTER MRITIHS-DEVICE LETTER NEMSPFLPER BOOK 
COMMRND: 
insert 
modifg-tree 
modifg-node 
delete 
Command 
: (lexi 
con) 
INHERITED DEFRULTS RETRI~ DELETE take \[\] \[\] 
put \[\] \[\] 
find \[\] \[\] 
speech-action \[\] \[\] 
mental-action \[\] \[\] eat 
\[\] \[\] 
In9est \[\] \[\] 
drink \[\] \[\] nova \[\] \[\] 
attack \[\] \[\] 
0o It \[\] ~ Rbort \[\] 
Lisp Lis 
OBJECT 
PHYSICAl-OBJECT MENTAL-OBJECT 
I~ -ALIVE MATERIAL INFORMATION 
RHinO1 PERSON DEVICE CONTAINER DOCUMEHT EDITOR COMPILER DAMS DATABASE 
VEHICLE COMPUTER MRITING-DEVICE LETTER NEWSPAPER BOOK 
COMMRND: 
insert 
modifg-tree 
modifg-node 
delete 
Command 
: (lex icon)l 
Lisp Lis 
Figure 5. Entering computer-user, II. 
284 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
PHYSICAL-OBJECT 
+ALIVE -ALIVE 
PLRNT CAERTURE ARTIFACT NATURAL 
ANIMAL PERSON DEVICE CONTAINER DOCUMENT 
COMPUTER-USER VEHICLE COMPUTER URITING-DEVICE LETTER NEUSPEPER BOOK 
COMMAND: 
insert 
modify-tree 
modify-node 
delete 
Command 
: (lexi 
con) I 
Figure 5A. Entering computer-user, III. 
Lisp Lis 
ancestor of the inherited slot values (in this case, 
'action') and suggesting to the enterer all the descen- 
dants of this ancestor node that are not currently listed 
as the slot values. For slots whose value domains are 
not concepts but rather property values, the help mech- 
anism simply lists the complete list of values for discrete 
domains and the appropriate ranges for the continuous 
ones. In our example, the enterer adds operate to the 
values of the agent-of slot. Another important ability 
(not illustrated in the example) is adding new slots to 
various frames and then supplying fillers for them. 
When the slot filling stage is completed (this stage of 
the process is illustrated in Figure 5), it is necessary to 
check whether the new node is distinct from its parent 
and siblings. In our example, the new concept does not 
have siblings, therefore only the former distinction is 
tested. The parent distinction check is performed auto- 
matically -- the system will alert the enterer if there has 
been no changes (except for the 'isa' slot filler) to the 
frame displayed for modification at the Fill stage. If this 
happens, the new concept is discarded as a duplicate of 
the parent node. In our example, an additional value has 
been added to the agent-of slot. The sibling distinction 
check is facilitated in the LMS by allowing the enterer 
to view the frames for the concept and its siblings 
alongside one another. In case no distinction can be 
found between the new concept and one of its siblings, 
the new concept is deleted as a duplicate. In general, 
before deleting a new concept because of duplication, 
the enterer makes an effort to save it by changing at 
least one slot value in either of the two duplicate frames. 
This may involve adding an existing concept or a 
property value from the current domain. However, in 
some cases, a new concept or a modification of a 
property domain may be required. Each such case is 
reported to the manager. 
3.2 THE MANAGER MODE: ADDING THE CONCEPT 
MANAGE(-DATABASE) 
The enterer first decides to insert the new concept 
manage(-database) as a sibling of operate and, there- 
fore, selects physical-action as the parent node. When 
the frame menu is displayed, however, he discovers a 
major discrepancy -- manage(-database) requires the 
'mental-object' filler for the object slot. The enterer 
then makes an attempt to attach the new concept as a 
child of the mental-action node but cannot accept 
reaction, cognition, and perception as the siblings of 
manage(-database) for reasons of significant conceptual 
dissimilarity. At this point, the enterer decides to refer 
the matter to the manager. This is accomplished by 
writing a report about the situation and sending it 
through the mail system. 
At this point the responsibility for processing this 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 285 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
PHYS'rCBL-$TBTE HEHTAL-STATE PHYSICRL-RCIION $PEE\[Show Attribut~Sll nEHTAL-ACTION J--IOisplay 5ubtree I1~ 
BOVE 0 P E R AT E~~.,.,~ ASSFRIION REACTION COGNITION PERCEPTION 
REO-THFO REO-ACTIOH 
COMMAND: 
insert 
modiFy-tree 
modify-node 
delete 
ConMend 
naneger 
)l 
Lisp Lis 
Figure 6. Changing the descendents for mental-action, I. 
concept passes to the manager. In addition to the 
enterer's message the manager also has access to the 
enterer's attempts to insert manage(-database) into the 
hierarchy. The manager is, naturally, capable of han- 
dling all the enterer's tasks; but the LMS manager mode 
has additional functionality. The enterer is allowed to 
perform only one operation on the 'isa' hierarchy -- 
adding leaves to it. The manager can perform all the 
operations on this tree u insert new nodes anywhere in 
it; delete nodes (either with their subtrees or without, in 
which case the children of the deleted node become 
children of the latter's parent); and move subtrees to a 
different position. The manager is also entitled to create 
additional property sets (domains) and add and delete 
values in the existing ones. Finally, the manager's 
authority also covers adding, deleting and modifying the 
value domains of all slots in all concept frames. Each of 
these capabilities is supported by LMS routines. 
In our example, the manager has to reorganize the 
subtree whose root is mental-action. First, he finds the 
necessary node and calls a menu of operations on a 
node (Figure 6). Clicking on 'delete-child', he obtains 
another menu, in which he indicates that he wants to 
detach the children of the node. The result of this 
operation is illustrated in Figure 7. At this point the 
insertion of the two intermediate concepts: computer- 
mental-action and non-computer-mental-action can be 
performed (which is supported in the manner illustrated 
in 3.1 above). During the insertion process the manager 
acts exactly in the same manner as an enterer. Tempo- 
rarily disregarding the detached children, he inserts the 
intermediate concepts one by one. 
At the next stage, the former children of mental- 
action are reconnected to the hierarchy as children of 
non-computer-mental-action. Once again, the manager 
acts here as an enterer, filling the frame slots and 
running the sibling and parent distinction checks. Next, 
manage(-database) is attached as a child of the com- 
puter-mental-action node. Again, all the insert-time 
checks are run. At this point the manager decides to 
develop the computer-mental-action node further de- 
ductively, that is, without waiting for empirical evi- 
dence for this to accumulate. He decides to enter the 
nodes write-code and use-interactive-system as siblings 
of manage(-database). This operation is entirely at the 
manager's discretion and expresses the manager's un- 
derstanding of the computer world and his expectations 
concerning the concepts that will have to be added to 
the ontology in the future. After these concepts have 
been added to the world, the subtree with the root 
mental-action will look as in Figure 8. 
286 Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
STATE ACTION 
PHYSICAL-STATE IIEHTflL-$TflTE PHYSICAL-ACTION SPEECH-flCIION nENTflL-flCTION 
hOVE OPERATE IHSEST _~_.~ES T ASSERTION 
\ 
REO-ZRFO REO-RCTION 
REACTION COGNITION PERCEPTION 
Figure 7. Changing the descendents for mental-action, II. 
COflflRND: 
insert 
modi£y-tree 
nodify-node 
delete 
Coes~and 
: (le xtcon i 
nanager 
)l 
Lisp LIs 
4. DISCUSSION 
The TRANSLATOR LMS has been implemented only 
in its initial stage, so that its functionality at present is 
more constrained than it will be when the LMS attains 
full capacity. We would like to discuss here the func- 
tionality upgrades under development at the present 
time. 
First, the manager's ability to maintain integrity and 
consistency in the lexicons must be further supported. 
This capability includes modifications of the existing 
concept lexicon entries which are affected by the inser- 
tion of a new concept by an enterer or the manager or by 
the manager's revision of the domain of a frame slot. At 
present, the LMS can automatically find and modify the 
values in slots complementary to the ones modified. For 
example, in our first sample session, after the enterer 
adds 'operate' to the agent-of slot of computer-user, the 
system automatically adds computer-user to the agent 
slot of 'operate.' But more should be expected. Thus, in 
our second sample session, after the manager inserts the 
computer-mental-action and non-computer-mental- 
action as the children of mental-action,' the LMS 
should be able to generate a suggestion that he consider 
a symmetrical modification in the sibling branch (phys- 
ical-action), which would make operate a child of 
computer-physical-action.' In general, we feel that the 
LMS should provide more heuristic support for the 
manager's thought processes concerning ontology mod- 
ifications. 
This latter modification, along with the one the 
manager actually executed in the sample session, would 
be a step in the transition from the world ontology in 
Figure 3 to the computer subworld ontology. Some of 
these changes accurately reflect the differences between 
the world and the subworld, i.e., in this case, the 
distinction between concepts of computer-related and 
non-computer-related is central for the subworld but 
not for the world. Other changes in the ontology include 
the correction of mistakes and the refining of the 
hierarchies suggested by the accumulation of data and 
experience. Thus, we will not be surprised if additional 
data will provide evidence for making speech-action a 
descendant of mental-action instead of action. 
An additional source of insight into the improvement 
of the LMS will be provided by its use. Thus, it is 
difficult to know a priori how many enterers a single 
manager will be able to supervise; what forms of the 
interaction between the enterers and the manager are 
most effective; whether it is possible to support a 
concurrent mode of operation in which more than one 
enterers work on one hierarchy simultaneously; or 
should there be levels of priority assigned to the mes- 
sages from enterers to the manager or mechanisms for 
clustering similar messages (if the messages are forced 
Computational Linguistics, Volume 13, Numbers 3-4, July-December 1987 287 
Sergei Nirenburg and Victor Raskin The Subworld Concept Lexicon and the Lexicon Management System 
MENTAL-ACTION 
COMPUTER-M-A AOX-COMPUTER-M-A 
AANAGE-DB MRITE-COOE USE-IHTER-SYSTEA REACTION COGNITION PERCEPTION 
COMMRND: 
insert 
modify-tree 
modify-node 
delete 
Connand 
: (le 
xicon 
~mna9er ) | 
Lisp Lis 
Figure 8. Changing the descendents for mental-action, III. 
into a certain format). A separate concern is a detailed 
set of on-line instructions for quick reference both by 
the enterers and the manager. It should be added to the 
LMS in the tutorial, reference-manual and keyword- 
based help mode format. 
So far in this section, we have been discussing the 
depth-wise improvements to the TRANSLATOR LMS. 
The breadth-wise development concerns the analysis 
and generation lexicons. The work on those can be 
completed only after the subworld concept lexicon is 
already reasonably complete. 
In the analysis lexicons, indexed by the words or 
word equivalents, the entries will contain conceptual 
information from the subworld concept lexicon. Many 
words, however, will not have a concept-based lexicon 
entry but will contain information triggering certain 
parsing procedures. Some other entries will include 
both of these types of information. Every entry in an 
analysis lexicon contains syntactic information, and the 
current LMS already includes an interactive aid for 
acquiring it for English. It appears that generation 
lexicons are, in fact, indexed not only by subworld 
concepts but also by rhetorical and pragmatic markers 
in the representation of a text obtained after analysis 
because both typically have lexical -- as well as gram- 
matical -- expression in natural language. 
Acknowledgements. This research was supported, in 
part, by the National Science Foundation under Grant 
DCR-8407114 and, in part, by the Air Force Systems 
Command, Rome Air Development Center, Griffiss Air 
Force Base, NY 13441-5700, and the Air Force Office of 
Scientific Research, Boiling Air Force Base, DC, 20332, 
under contract number F30602-85-C-0008, to the North- 
east Artificial Intelligence Consortium. We would also 
like to thank Irene Nirenburg who implemented the 
LMS, and Sabine Bergler for a number of useful discus- 
sions of the subject matter. 

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