Semantic Interpretation Using KL-ONE 1 
Norman K. Sondheimer 
USC/Information Sciences Institute 
Marina del Rey, California 90292 USA 
Ralph M. Weischedel 
Dept. of Computer & Information Sciences 
University of Delaware 
Newark, Delaware 19716 USA 
Robert J. Bobrow 
Bolt Beranek and Newman, Inc. 
Cambridge, Massachusetts 02238 USA 
Abstract 
This paper presents extensions to the work of Bobrow and 
Webber \[Bobrow&Webber 80a, Bobrow&Webber 80b\] on 
semantic interpretation using KL-ONE to represent knowledge. 
The approach is based on an extended case frame formalism 
applicable to all types of phrases, not just clauses. The frames 
are used to recognize semantically acceptable phrases, identify 
their structure, and, relate them to their meaning representation 
through translation rules. Approaches are presented for 
generating KL-ONE structures as the meaning of a sentence, for 
capturing semantic generalizations through abstract case frames, 
and for handling pronouns and relative clauses. 
1. Introduction 
Semantic interpretation is the process of relating the 
syntactic analysis of an utterance ",o its meaning representatioh. 
Syntactic analyses associate immediate constituents with their 
syntactic function in a matrix constituent, e.g., the sentence 
"Send him the message that arrived yesterday.", has a syntactic 
analysis in RUS \[Bobrow 78\] as shown in Figure 1.2 The elements 
of the meaning representation are the objects, events, and states 
of affairs perceived by the speaker. The relationships between 
these entities will be called semantic functions. The basis for our 
semantic processing scheme is a familiar one based on that of 
case frames used to describe clausa structure \[Bruce 75\]. Our 
case frames are used for all phrase types: clauses, noun phrases, 
prepositional phrases, etc. We choose to represent both the 
syntactic and semantic analyses in the knowledge representation 
language KL-ONE \[Brachman&Schmolze 82, Schmolze&Lipkis 
83, Moser 83\]. The essential properties for the meaning 
representations constructed are that each concept represents a 
semantic constituent and each of its roles identifies the semantic 
function of one of its immediate constituents. Figure 23 gives an 
analysis of the example sentence above. We have picked a 
constituent structure and names for semantic functions fitting the 
computer mail application of .the the Consul project at 
USC/Information Sciences Institute \[Kaczmarek 83\]. The exact 
details of the analysis are not critical; the essential point is that 
1This material is based upon work supported in part by the Defense Advanced 
Research Projects Agency under Contract Numbers MDA 903-81-C-0335, ARPA 
Order No. 2223, and N00014-77-C-0378, ARPA Order No. 3414. Views and 
conclusions contained in this paper are the authors' and should not be 
interpreted as representing the official policies of DARPA, the U.S, Government, 
or any person or agency connected with them. 
2We use this sentence to illustrate many of the points in this paper. Assume 
that "yesterday" modifies "arrived". 
3All of the KL-ONE diagrams in this paper are simplified for expository 
purposes, 
semantic interpretation relates a' phrase's analysis based on 
syntactic criteria to one based on semantic criteria. 
Clause 
Head: Send 
Indire~-I Object: Noun Phrase 
Head: Him 
Direct Object Noun Phrase 
Head: Message 
Article: The 
Relative: Clause 
Head: Arrive 
Subject: That 
Time: Yesterday 
Figure 1: Syntactic Analysis of "Send him the message that 
arrived yesterday.". Simplifications in tense, determiners and 
numbers are for the sake of presentation. 
Figure 2: Meaning Representation of "Send him the message 
that arrived yesterday.". Simplification on determiners and the 
further-constraints structure for the sake of presentation. 
I01 
Our framework does not assume that a syntactic analysis of 
a complete sentence is found before semantic interpretation 
begins. Rather, the implemented semantic interpreter proceeds 
incrementally as the grammar proposes the syntactic function of 
an immediate constituent; this moc~el of communication between 
syntax and semantics has been termed a cascade \[Woods 
80, Bobrow&Webber 80b\]. 
To achieve semantic interpretation, some well.known types 
of knowledge need "to be employed, e.g., selection restriction 
information (often represented using semantic features), 
structural information (often encoded in case frames), and 
translation information (often defined with various kinds of 
projection rules). 
Some of the difficulties in representing and applying this 
knowledge include the following: 
1. Translation rules (projection rules) for generating 
correct meaning representations must be defined. 
We have been able to define modular projection rules 
that make use of the inheritance properties of KL- 
ONE. 
2. Since much of the knowledge for a particular 
application is necessarily domain specific, it is 
important to organize it in a way to ease extension of 
the knowledge base and to ease moving to a new 
domain. 
3. Since distributional restrictions require specific 
semantic features, pronouns and other semantically 
neutral terms not necessarily having those features 
must be accepted wherever they are consistent with 
the expected type of noun phrase. 
4. The inter-constituent relationships arising in relative 
clauses must be consistent with all selection 
restrictions and be represented in the resulting 
meaning representation. 
This paper addresses each of these issues in turn. 
We are building on techniques presented by Bobrow and 
Webber \[Bobrow&Webber 80a, Bobrow&Webber 80b\]. This 
paper describes the system currently in use at USC/Information 
Sciences Institute. The basic framework is reviewed in Section 2. 
Section 3 presents the translation mechanism \[Sondheimer 84\]. 
Capturing semantic generalizations is the topic of Section 4. 
Sections 5 and 6 discuss issues regarding pronouns and relative 
clauses, respectively. Related work is identified in Section 7. The 
final section summarizes the results, and identifies further work. 
A very brief introduction to KL-ONE is provided in an appendix. 
2. Background 
The framework being developed uses a frame for each 
semantically distinguishable type of phrase. Thus, a frame will be 
required for each class of phrase having a uniq.ue combination of 
. semantic distribution, 
- selection restrictions on constituents making up the 
phrase, and 
-_assignment of semantic relations to syntactic 
function. 
It is likely that the frames will reflect the natural categories of 
descriptions of objects, events, actions, and states of affairs in 
any particular application. For example, in the computer mail 
domain, the following are some frames that have been useful: 
- Clauses describing the sending of messages: SEND. 
CLAUSE 
- Clauses describing message arrival: ARRIVE. 
CLAUSE 
- Noun phrases describing messages: MESSAGE-NP 
-Noun phrases describing senders and recipients: 
USER-NP 
In the framework developed by Bobrow and Webber 
\[Bobrow&Webber 80a, Bobrow&Webber 80b\], for each frame, 
each possible immediate constituent is associated by syntactic 
function with a case or slot. The clause frames have slots 
identified as head, subject, 4"direct object, indirect object, etc. 
Noun phrase frames have slots for the head, adjective modifiers, 
article, etc. Each slot specifies the fillers that are semantically 
acceptable, whether it is required or optional, and the number of 
times it may be filled in a phrase. The constraints on fillers of 
frames' slots are stated in terms of other frames, e.g., the direct 
object of a SEND-CLAUSE must be a MESSAGE.NP, or in terms 
of word senses and categories of these senses. Some example 
word sense categories are: 
• Message description nouns, such as "message" or 
"letter": MESSAGE.NOUN 
• Information transmission verbs, such as "send" or 
"forward": TRANSMISSION.VERB 
In our domain the constraint on the subject of an ARRIVE- 
CLAUSE is that it satisfies the MESSAGE.NP frame. A constraint 
on the head of the MESSAGE.NP frame is that it is a word sense 
in the category MESSAGE.NOUN. 
Frames are represented as KL.ONE concepts. Case slots 
appear as roles of concepts. 5 Semantic constraints on what can 
fill a case slot are encoded as the value restrictions of roles. 
These value restrictions are concepts representing frames, word 
senses, or word sense categories. Number restrictions on roles 
show the number of times the syntactic function may be realized. 
A required slot is marked by the number restriction on its role 
having a minimum of 1; an optional slot has a number restriction 
with a minimum of 0 and a maximum greater than 0. A phrase is 
said to instantiate a given frame X if and only if its immediate 
constituents satisfy the appropriate value and number restrictions 
of all of X's roles. 6 The collection of frames and word-sense 
4Subject, object, etc. refer to logical roles rather than surface syntactic ones. 
51t is possible to associate roles with semantically defined subsets of other 
roles, e.g., to assign separate roles to uses of color adjectives, size adjectives, 
etc. This is an important convenience in constructing frames but not crucial to 
our discussion. 
6A recognition algorithm for this representation has been 
presented \[Bobrow&Webber 80b\] and several others have been developed since 
then. Thase will be presented in separate reports. 
102 
information is called a Syntaxonomy (for syntactic taxonomy), 
since it encodes knowledge regarding semantic interpretation in 
a hierarchy of syntactic classes. 
3. Translation Rules 
To achieve the mapping from syntactic analysis to meaning 
representation, translation rules are associated with individual 
frames. Though the rules we give generate KL-ONE structures as 
the meaning representation, other translation rules could be 
developed for generating forms in a different target 
representation language. 
Any KL.ONE concept C representing a frame has an 
associated concept C' representing the main predicate of the 
translation. For example, the translation of SEND-CLAUSE is the 
concept Send-mail. Translations are stored in data attached to 
the frame; we label this data TRANSLATION. 
The translation rules themselves can be associated with 
individual case slots. When inheritance results in more than one 
translation rule for a case slot, the one originating from the most 
specific frame in the hierarchy is selected. 7 
Suppose we are building the translation C' of a matched 
frame C. One common translation rule that could appear at a role 
R of C is (Paraphrase-as R'). This establishes the translation of 
the filler of R as the filler of R' at concept C'. For example, the 
indirect object slot of SEND-CLAUSE has the rule "(Paraphrase- 
as addressee)" to map the translation of the noun phrase in the 
indirect object position into the addressee role of the Send-mail. 
Another rule form, (Attach-SD sf), takes a semantic 
function sf as an argument and attaches the translation of the 
constituent filling R as the filler F of sf. A example of its use in the 
processing of relative clauses as described in Section 6. Attach- 
SD differs from Paraphrase-as by having facilities to establish a 
role from F to C'. This automatic feature is essentially the 
opposite of Paraphrase.as, in that a semantic function runs from 
the embedded constituent to its matrix phrase. 
Another rule form is not a translation rule per se, but stores 
data with the syntactic concept representing the syntactic 
analysis of the phrase. The data could be checked by other 
(conditional) translation rules. 
Underlying these forms and available for more complex 
types of translation is a general mechanism having the form 
"source = = > goal." The source identifies the structure that is to 
be placed at the location identified by the goal. The formalism for 
the source allows reference to arbitrary constants and concepts 
and to a path through the concepts, roles, and attached data of a 
KL-ONE network. The goal formalism also shows a path through 
a network and may specify establishment of additional roles. 
A separate test may be associated with a translation rule to 
state conditions on the applicability of a rule. If the test is false, 
the rule does not apply, and no translation corresponding to that 
role is generated. The most common type of condition is 
(Realized-Function? role), which is true if and only if some 
7There is also an escape mechanism that allows inheritance of all rules not 
indexed to any role. 
immediate constituent fills that role in the analysis. It can be used 
as an explicit statement that an optional role is translated only if 
filled or as a way of stating that one constituent's translation 
depends on the presence of another role. Additional conditions 
are (EMPTY-RC)LE?role), which checks that role is not filled, and 
(ROLE-FILLER? role class), which checks that the filler of role is 
of type class. Since all three take a role name as argument, they 
may be used to state cross,dependencies among roles. 
Figure 3 contains some of the frames that allow for the 
analysis .of our example. The treatment of the pronoun and 
relative clause in the example sentence of Section I will be 
explained in Sections 5 and 6. 
4.Capturing Semantic Generalizations 
via Abstract Case Frames 
Verbs can be grouped with respect to the cases they 
accept \[Simmons 73, Celce-Murcia 76, Gawron 83\]; likewise, 
groups exist for nouns. A KL-ONE syntaxonomy allows 
straightforward statement of common properties, as well as 
individually distinct properties of group members. Abstract case 
frames are semantic generalizations applicable across a set of 
the familiar sort of concrete frames. Properties common to the 
generalization can be defined at the abstract frames and related 
to the concrete frames through inheritance. 
The use of time modification in "that arrived yesterday" is 
the same as that of other verbs describing completion of an 
activity, e.g., "come", "reach", and "finish". A general frame for 
clauses with these verbs can show this role. The concrete frames 
for clauses with verbs in this group are subconcepts and thereby 
accept the time modifier (see Figure 4). The concrete frames can 
restrict both the number and type of time modifiers, if necessary. 
Translation rules associated with this time role can also be 
restricted at the concrete frames. 
Some modifiers dramatically affect the translation of entire 
phrases, as in the partitive modifier "half of". A description of 
"half of" some individual entity (as opposed to a set of entities) 
may not have the same distribution. For example, "Delete this 
message from my directory.", makes sense, but "Delete half of 
this message from my directory.", does not. This can be easily 
stated through an abstract frame for the basic message 
description specialized by two concrete frames(see Figure 5). 
A related case is "toy X." The translation of "toy X" is 
certainly different from that of X, and their distributions may differ 
as well. This may be handled in a way similar to the partitive 
example. 8 This class of examples points out the limits of case 
frame systems. Other modifiers, such as "model" and "fake", are 
easily recognizable. However, more complex modifiers also make 
the same distinctions, e.g., "The gun that was a fake was 
8An'interesting alternative is .to show the toy modifier as an optional role on an 
abstract frame for object descriptions. Underneath it could be an abstract frame 
distinguished only by requiring the toy modification'role. All appropriate 
inferences associated with descriptions of toys could De associated with this 
concept. Frames for the basic descriptions of specific object types could be 
placed underneath the object description frame. These could recognize "toy X". 
Our systems invoke the KL-ONE classifier after the recognition of each phrase 
\[Schmolze&Lipkis 83\]. in this case, classification will result in identification of the 
phrase ss a kind of both X description and toy description allowing translation to 
show what is known about both without creating a "toy X" frame by hand. We 
have not completely analyzed the affect of this strategy on the translation system. 
103 
TB A kit21 A TIt~kJ * 
~slation Rule: If (Realized-Function? Indirect Object) 
then (Paraphrase-as addressee) 
~slation Rule: (Paraphrase.as message) 
TRANSLATION: ) 
Min:l Max:l_~ 
Subject Min:O Max:l Translation Rule: If (Realized.Function? Subject) 
then (Paraphrase-as message) 
Time Min:0 Max:l Translation Rule: If (Realized.Function? Time) 
then(Paraphrase.as completion-time.interval) 
TRANSLATION: 
Min:l Max:~ 
Determiner Min:l 
Relative Min:O Max:oo Translation Rule: If (Realized-Function? Relative) 
then (Attach.SD further.constraint) 
Figure 3: Some frames used for "Send him the message that arrived yesterday." 
.ti 
Figure 4: A fragment of the syntaxonomy. Double arrows are 
subc relationships, i.e., essentially "is-a" arcs. Not all roles are 
shown. 
partitive ~ partitive 
Min:O Max:O Min:l Max:l 
Figure 5: Syntaxonomy for partitives. 
104 
John's.", and "The gun that was made of soap was John's.". 
Viewing our semantic interpretation system as a special purpose 
infereoce system, it seems prudent to leave the recognition of the 
type of these "guns" to more general.purpose reasoners. 
Abstract case frames have significantly eased the 
development and expansion of semantic coverage within our 
application by helping us to focus on issues of generality and 
speciiicity. The new frames we add have many slots established 
by inheritance; consistency has been easier to maintain; and the 
structure of the resulting syntaxonomy has helped in debugging. 
5. Semantically Neutral Terms 
Case frames are an attempt to characterize semantically 
coherent phrases, for instance, by selection restrictions. In 
computational linguistics, selection restrictions have been 
applied to the constituents that are possible fillers rather than to 
what the constituents denote. For example, the restriction on the 
direct object of a SEND-CLAUSE is MESSAGE-NP, rather than 
messages. Problems with using such approximations in parsing 
are discussed in \[Ritchie 83\]. 
For many natural language interfaces, a noun phrase's 
internal structure gives enough information to determine whether 
it satisfies a restriction, s However, there are forms whose 
semantic interpretation does not provide enough information to 
guarantee the satisfaction of a constraint and yet need to be 
allowed as fillers for slots. These include pronouns, some 
elliptical forms, such as "the last three", and otherneutral noun 
phrase forms, such as "the thing" and "the gift". This also 
includes some nonlexical gestural forms like the input from a 
display that shows where the user pointed (literally or via a 
mouse). We refer to all of these as sernantica//y neutra/terms. A 
semantic interpretation system should accept such forms without 
giving up restrictions on acceptable semantic categories. 
However, these forms cannot, in general, appear everywhere. In 
discussing computer mail, "1 sent him" should be considered 
nonsense. 
Bobrow and Webber \[Bobrow&Webber 80b\] propose a 
general strategy for testing the compatibility of a constituent as a 
slot filler based on non-incompatibility. The current system at 
USC/ISI takes a conservative view of this proposal, developing 
the idea for only neutral reference forms. All noun phrase types 
displaying neutral reference are defined as instances of the 
concept NeutraIReference.NP. Furthermore, disjointness" 
relations are marked between the various subclasses of neutral 
references and those classes of explicit descriptions which have 
nonintersecting sets of potential references. During 
interpretation, when such a NeutralReference-NP is proposed as 
a slot filler, and that concept is not disjoint from the value 
restriction on the slot, it is accepted. 
In addition, since the slot restriction and the filler each have 
meaning of their own, e.g., "he" describes a human male in the 
computer mail domain, the translation should show the 
contribution of both the neutral term and the constraint on the 
slot. When the neutral form is qualified as a constituent by the 
system, both the neutral form and the selection constraint are 
9Clearly, misreference also intederes with this method \[Goodman 8,3\], as does 
personification, metonymy and synecdoche. We propose other methods for these 
last phenomena in \[Weischedel 84, Weischedel 83\]. 
remembered. When it is time to produce the translation, the 
translation rule for the slot applies to a concept which is the 
conjunction of the translations of the neutral reference form and 
the restriction. 
Part of the network that supports the translation of "he" in 
the example of section 1 is shown in Figure 6. Referring to 
Figures 2 and 3, the effect of a reference to a male where a 
reference to a computer-user was expected can be seen. 
~ANSLATION: sex 
I Head Min:l Max:l 
~TRANSLATION: 
Figure 6: Network for "he." Note that computer User is a 
subconcept of Person. 
6. Inter-Constituent Relationships: Relative Clauses 
In relative clauses, the constraint on the slot filled by the 
relative pronoun or the trace 1° must be satisfied by the noun 
phrase that the relative clause modifies. In addition, the 
translation of the noun phrase must reflect the contribution of the 
use of the pronoun or trace in the relative clause. For example, in 
"Send him the message that arrived yesterday", the constraint on 
the subject of "arrive" must be satisfied by the noun phrase of 
which it is a part. Further, translation must result in co-reference 
within the meaning representation of the value of the message 
role of the Arrival.mail concept and the value of the message role 
of the Send.mail concept (see Figure 2). This is a form of inter- 
constituent relationship. 
Our system processes relative clauses by treating the 
relative pronouns and trace elements as neutral reference forms 
(just as in the pronominal cases discussed in Section 5 and by 
storing the constraints on the head of the relative clause until 
they can be employed directly. In our example, the noun phrase 
"that" is seen as a Trace-NP, a kind of NeutralReference.NP. 
The structure assigned "that" is compatible with MESSAGE-NP 
and hence acceptable. On translation, the Trace-NP is treated 
like a neutral reference but the role and unchecked constraint are 
recorded, as attached data on the instantiated case frame that 
results from parsing the arrival clause. In the example, the facts 
that a Trace.NP is in the subject role and that a Message.NP is 
required are stored. That constraint is tested against the 
classification of the matrix noun phrase when the clause is 
proposed as a relative clause modifier. 11 
10The RUS parser which we employ supplies a "trace" to establish • syntactic 
place holder with reduced relatives. 
11 If the use of the relative pronoun or trace is inside • phrase inside the relative 
clause, as in "the town from which I come", the role and constraint will be passed 
upward twice, 
105 
If that constraint is satisfied, the fact that the relative 
pronoun and noun phrase co-refer is recorded. When the entire 
noun phrase is processed successfully, the appropriate co- 
references are established by performing (Attach-SD further- 
constraint) and by retrieving the translation associated with the 
role filled by the Trace-NP. This establishes co-reference 
between the concept attached by the translation rule and the 
: translation of the entire noun phrase. In our example, the 
translation of the noun phrase is made the value of the message 
role of the Arrival-mail. 
7. Related Work 
Our technique uses properties of KL-ONE to build a 
simplified, special-purpose inference engine for" semantic 
interpretation. The semantic processor is separate from both 
syntactic and pragmatic processing, though it is designed to 
maintain well-defined interaction with those components through 
Woods's cascade model of natural language processing \[Woods 
80\]. Uniform methods include logic grammars \[Pereira 
83, Palmer 83\] and semantic grammars\[Burton 77, Hendrix 
78, Wilensky 80\]. Logic grammars employ a Horn-clause theorem 
prover for both syntactic and semantic processing. Semantic 
grammars collapse syntactic and semantic analysis into an 
essentially domain.specific grammar. Semantic interpretation is 
handled through unification in some evolving systems, such as 
PATTR-II \[Robinson 83\]. 
Several recent systems have separate semantic 
interpretation components. Hirst \[Hirst 83\] uses a Montague- 
inspired approach to produce statements in a frame language. 
He uses individual mapping rules tied to the meaning-affecting 
rules of a grammar. Boguraev \[Boguraev 79\] presents a semantic 
interpreter based on patterns very similar to those of our case 
frames. The meaning representation it produces is very similar to 
the structure of our case frames. 
8. Conclusion 
We have presented approaches to typical difficulties in 
building semantic interpreters. These have included a sketch of a 
translation system that maps from the matched frames to KL-ONE 
meaning representations. The idea of abstract case frames and 
applications of them were introduced. Finally, ways of accepting 
neutral references and allowing for the inter-constituent 
constraints imposed by relative clauses were presented. 
Our experience indicates that KL-ONE is effective as a 
means of building and employing a library of case frames. The 
basic approach is being used in research computer systems at 
both USC/Information Sciences Institute and Bolt Beranek and 
Newman, Inc. 
Of course, many problems remain to be solved. Problems 
currently under investigation include: 
- Robust response to input that appears semantically 
ill.formed, such as using an unknown word, 
- A general treatment of quantification, 
- Treatment of.conjunction, 
. Feedback from the pragmatic component to guide 
semantic interpretation, 
• Generation of error messages (in English) based on 
the case frames if the request seems beyond the 
system's capabilities, 
- Understanding classes of metonymy, such as "Send 
this window to Jones," and 
• Provision for meaningful use of nonsense phrases, 
such as "Can I send a package over the ARPAnet?" 
I. Brief Description of KL-ONE 
KL-ONE offers a rigorous means of specifying terms 
(concepts) and basic relationships among them, such as 
subset/superset, disjointness, exhaustive cover, and relational 
structure. Concepts are denoted graphically as ovals. Concepts 
are Structured objects whose structure is indicated by named 
relations (ro/es) between concepts. Roles are drawn as arcs 
containing a circle and square. The concepts at the end of the 
role arcs are said to be va/ue restrictions. In addition, roles have 
maximum and minimum restrictions on the number of concepts 
that can be related by the role to the concept at the origin of the 
arc. Concepts can also have data attached to them, stored as a 
property list. Finally, the set of concepts is organized into an 
inheritance hierarchy, through subc relations drawn with double. 
line arrows from the subconcept to the superconcept. 
All of the KL-ONE diagrams in the text are incomplete; for 
instance, Figures 3 and 5 focus on different aspects of what is 
one KL-ONE structure. In figure 3, the diagram for SEND- 
CLAUSE specifies the concepts of "send" clauses. They have 
exactly one head, which must be the lexical concept "send." 
Theymust have a direct object which is a MESSAGE.NP, and 
they optionally have an indirect object which is a USER-NP. 
Figure 5 shows that SEND-CLAUSE's are MESSAGE- 
TRANSMISSION-CLAUSE's, which are a type of CLAUSE. 
The meaning representation, Figure 2, generated for "Send 
him the message that arrived yesterday" consists of the concept 
Send-mail, having an addressee which is a Computer-User and a 
message which is ComputerMail. 
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